CINXE.COM

<!DOCTYPE html> <html class="client-nojs vector-feature-language-in-header-enabled vector-feature-language-in-main-page-header-disabled vector-feature-sticky-header-disabled vector-feature-page-tools-pinned-disabled vector-feature-toc-pinned-clientpref-1 vector-feature-main-menu-pinned-disabled vector-feature-limited-width-clientpref-1 vector-feature-limited-width-content-enabled vector-feature-custom-font-size-clientpref-1 vector-feature-appearance-pinned-clientpref-1 vector-feature-night-mode-enabled skin-theme-clientpref-day vector-toc-available" lang="en" dir="ltr"> <head> <meta charset="UTF-8"> <title>Ensemble learning - Wikipedia</title> <script>(function(){var className="client-js vector-feature-language-in-header-enabled vector-feature-language-in-main-page-header-disabled vector-feature-sticky-header-disabled vector-feature-page-tools-pinned-disabled vector-feature-toc-pinned-clientpref-1 vector-feature-main-menu-pinned-disabled vector-feature-limited-width-clientpref-1 vector-feature-limited-width-content-enabled vector-feature-custom-font-size-clientpref-1 vector-feature-appearance-pinned-clientpref-1 vector-feature-night-mode-enabled skin-theme-clientpref-day vector-toc-available";var cookie=document.cookie.match(/(?:^|; )enwikimwclientpreferences=([^;]+)/);if(cookie){cookie[1].split('%2C').forEach(function(pref){className=className.replace(new RegExp('(^| )'+pref.replace(/-clientpref-\w+$|[^\w-]+/g,'')+'-clientpref-\\w+( |$)'),'$1'+pref+'$2');});}document.documentElement.className=className;}());RLCONF={"wgBreakFrames":false,"wgSeparatorTransformTable":["",""],"wgDigitTransformTable":["",""],"wgDefaultDateFormat":"dmy", "wgMonthNames":["","January","February","March","April","May","June","July","August","September","October","November","December"],"wgRequestId":"ac24eb7d-f56c-4897-8ab6-fc43f06afaf0","wgCanonicalNamespace":"","wgCanonicalSpecialPageName":false,"wgNamespaceNumber":0,"wgPageName":"Ensemble_learning","wgTitle":"Ensemble learning","wgCurRevisionId":1254901523,"wgRevisionId":1254901523,"wgArticleId":22212276,"wgIsArticle":true,"wgIsRedirect":false,"wgAction":"view","wgUserName":null,"wgUserGroups":["*"],"wgCategories":["CS1 maint: DOI inactive as of November 2024","Articles with short description","Short description is different from Wikidata","All articles with unsourced statements","Articles with unsourced statements from December 2017","Articles with unsourced statements from January 2012","Ensemble learning"],"wgPageViewLanguage":"en","wgPageContentLanguage":"en","wgPageContentModel":"wikitext","wgRelevantPageName":"Ensemble_learning","wgRelevantArticleId":22212276, "wgIsProbablyEditable":true,"wgRelevantPageIsProbablyEditable":true,"wgRestrictionEdit":[],"wgRestrictionMove":[],"wgNoticeProject":"wikipedia","wgCiteReferencePreviewsActive":false,"wgFlaggedRevsParams":{"tags":{"status":{"levels":1}}},"wgMediaViewerOnClick":true,"wgMediaViewerEnabledByDefault":true,"wgPopupsFlags":0,"wgVisualEditor":{"pageLanguageCode":"en","pageLanguageDir":"ltr","pageVariantFallbacks":"en"},"wgMFDisplayWikibaseDescriptions":{"search":true,"watchlist":true,"tagline":false,"nearby":true},"wgWMESchemaEditAttemptStepOversample":false,"wgWMEPageLength":50000,"wgRelatedArticlesCompat":[],"wgCentralAuthMobileDomain":false,"wgEditSubmitButtonLabelPublish":true,"wgULSPosition":"interlanguage","wgULSisCompactLinksEnabled":false,"wgVector2022LanguageInHeader":true,"wgULSisLanguageSelectorEmpty":false,"wgWikibaseItemId":"Q245652","wgCheckUserClientHintsHeadersJsApi":["brands","architecture","bitness","fullVersionList","mobile","model","platform","platformVersion"], "GEHomepageSuggestedEditsEnableTopics":true,"wgGETopicsMatchModeEnabled":false,"wgGEStructuredTaskRejectionReasonTextInputEnabled":false,"wgGELevelingUpEnabledForUser":false};RLSTATE={"ext.globalCssJs.user.styles":"ready","site.styles":"ready","user.styles":"ready","ext.globalCssJs.user":"ready","user":"ready","user.options":"loading","ext.cite.styles":"ready","ext.math.styles":"ready","skins.vector.search.codex.styles":"ready","skins.vector.styles":"ready","skins.vector.icons":"ready","jquery.makeCollapsible.styles":"ready","ext.wikimediamessages.styles":"ready","ext.visualEditor.desktopArticleTarget.noscript":"ready","ext.uls.interlanguage":"ready","wikibase.client.init":"ready","ext.wikimediaBadges":"ready"};RLPAGEMODULES=["ext.cite.ux-enhancements","mediawiki.page.media","ext.scribunto.logs","site","mediawiki.page.ready","jquery.makeCollapsible","mediawiki.toc","skins.vector.js","ext.centralNotice.geoIP","ext.centralNotice.startUp","ext.gadget.ReferenceTooltips", "ext.gadget.switcher","ext.urlShortener.toolbar","ext.centralauth.centralautologin","mmv.bootstrap","ext.popups","ext.visualEditor.desktopArticleTarget.init","ext.visualEditor.targetLoader","ext.echo.centralauth","ext.eventLogging","ext.wikimediaEvents","ext.navigationTiming","ext.uls.interface","ext.cx.eventlogging.campaigns","ext.cx.uls.quick.actions","wikibase.client.vector-2022","ext.checkUser.clientHints","ext.growthExperiments.SuggestedEditSession","wikibase.sidebar.tracking"];</script> <script>(RLQ=window.RLQ||[]).push(function(){mw.loader.impl(function(){return["user.options@12s5i",function($,jQuery,require,module){mw.user.tokens.set({"patrolToken":"+\\","watchToken":"+\\","csrfToken":"+\\"}); }];});});</script> <link rel="stylesheet" href="/w/load.php?lang=en&modules=ext.cite.styles%7Cext.math.styles%7Cext.uls.interlanguage%7Cext.visualEditor.desktopArticleTarget.noscript%7Cext.wikimediaBadges%7Cext.wikimediamessages.styles%7Cjquery.makeCollapsible.styles%7Cskins.vector.icons%2Cstyles%7Cskins.vector.search.codex.styles%7Cwikibase.client.init&only=styles&skin=vector-2022"> <script async="" src="/w/load.php?lang=en&modules=startup&only=scripts&raw=1&skin=vector-2022"></script> <meta name="ResourceLoaderDynamicStyles" content=""> <link rel="stylesheet" href="/w/load.php?lang=en&modules=site.styles&only=styles&skin=vector-2022"> <meta name="generator" content="MediaWiki 1.44.0-wmf.4"> <meta name="referrer" content="origin"> <meta name="referrer" content="origin-when-cross-origin"> <meta name="robots" content="max-image-preview:standard"> <meta name="format-detection" content="telephone=no"> <meta name="viewport" content="width=1120"> <meta property="og:title" content="Ensemble learning - Wikipedia"> <meta property="og:type" content="website"> <link rel="preconnect" href="//upload.wikimedia.org"> <link rel="alternate" media="only screen and (max-width: 640px)" href="//en.m.wikipedia.org/wiki/Ensemble_learning"> <link rel="alternate" type="application/x-wiki" title="Edit this page" href="/w/index.php?title=Ensemble_learning&action=edit"> <link rel="apple-touch-icon" href="/static/apple-touch/wikipedia.png"> <link rel="icon" href="/static/favicon/wikipedia.ico"> <link rel="search" type="application/opensearchdescription+xml" href="/w/rest.php/v1/search" title="Wikipedia (en)"> <link rel="EditURI" type="application/rsd+xml" href="//en.wikipedia.org/w/api.php?action=rsd"> <link rel="canonical" href="https://en.wikipedia.org/wiki/Ensemble_learning"> <link rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/deed.en"> <link rel="alternate" type="application/atom+xml" title="Wikipedia Atom feed" href="/w/index.php?title=Special:RecentChanges&feed=atom"> <link rel="dns-prefetch" href="//meta.wikimedia.org" /> <link rel="dns-prefetch" href="//login.wikimedia.org"> </head> <body class="skin--responsive skin-vector skin-vector-search-vue mediawiki ltr sitedir-ltr mw-hide-empty-elt ns-0 ns-subject mw-editable page-Ensemble_learning rootpage-Ensemble_learning skin-vector-2022 action-view"><a class="mw-jump-link" href="#bodyContent">Jump to content</a> <div class="vector-header-container"> <header class="vector-header mw-header"> <div class="vector-header-start"> <nav class="vector-main-menu-landmark" aria-label="Site"> <div id="vector-main-menu-dropdown" class="vector-dropdown vector-main-menu-dropdown vector-button-flush-left vector-button-flush-right" > <input type="checkbox" id="vector-main-menu-dropdown-checkbox" role="button" aria-haspopup="true" data-event-name="ui.dropdown-vector-main-menu-dropdown" class="vector-dropdown-checkbox " aria-label="Main menu" > <label id="vector-main-menu-dropdown-label" for="vector-main-menu-dropdown-checkbox" class="vector-dropdown-label cdx-button cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--weight-quiet cdx-button--icon-only " aria-hidden="true" ><span class="vector-icon mw-ui-icon-menu mw-ui-icon-wikimedia-menu"></span> <span class="vector-dropdown-label-text">Main menu</span> </label> <div class="vector-dropdown-content"> <div id="vector-main-menu-unpinned-container" class="vector-unpinned-container"> <div id="vector-main-menu" class="vector-main-menu vector-pinnable-element"> <div class="vector-pinnable-header vector-main-menu-pinnable-header vector-pinnable-header-unpinned" data-feature-name="main-menu-pinned" data-pinnable-element-id="vector-main-menu" data-pinned-container-id="vector-main-menu-pinned-container" data-unpinned-container-id="vector-main-menu-unpinned-container" > <div class="vector-pinnable-header-label">Main menu</div> <button class="vector-pinnable-header-toggle-button vector-pinnable-header-pin-button" data-event-name="pinnable-header.vector-main-menu.pin">move to sidebar</button> <button class="vector-pinnable-header-toggle-button vector-pinnable-header-unpin-button" data-event-name="pinnable-header.vector-main-menu.unpin">hide</button> </div> <div id="p-navigation" class="vector-menu mw-portlet mw-portlet-navigation" > <div class="vector-menu-heading"> Navigation </div> <div class="vector-menu-content"> <ul class="vector-menu-content-list"> <li id="n-mainpage-description" class="mw-list-item"><a href="/wiki/Main_Page" title="Visit the main page [z]" accesskey="z"><span>Main page</span></a></li><li id="n-contents" class="mw-list-item"><a href="/wiki/Wikipedia:Contents" title="Guides to browsing Wikipedia"><span>Contents</span></a></li><li id="n-currentevents" class="mw-list-item"><a href="/wiki/Portal:Current_events" title="Articles related to current events"><span>Current events</span></a></li><li id="n-randompage" class="mw-list-item"><a href="/wiki/Special:Random" title="Visit a randomly selected article [x]" accesskey="x"><span>Random article</span></a></li><li id="n-aboutsite" class="mw-list-item"><a href="/wiki/Wikipedia:About" title="Learn about Wikipedia and how it works"><span>About Wikipedia</span></a></li><li id="n-contactpage" class="mw-list-item"><a href="//en.wikipedia.org/wiki/Wikipedia:Contact_us" title="How to contact Wikipedia"><span>Contact us</span></a></li> </ul> </div> </div> <div id="p-interaction" class="vector-menu mw-portlet mw-portlet-interaction" > <div class="vector-menu-heading"> Contribute </div> <div class="vector-menu-content"> <ul class="vector-menu-content-list"> <li id="n-help" class="mw-list-item"><a href="/wiki/Help:Contents" title="Guidance on how to use and edit Wikipedia"><span>Help</span></a></li><li id="n-introduction" class="mw-list-item"><a href="/wiki/Help:Introduction" title="Learn how to edit Wikipedia"><span>Learn to edit</span></a></li><li id="n-portal" class="mw-list-item"><a href="/wiki/Wikipedia:Community_portal" title="The hub for editors"><span>Community portal</span></a></li><li id="n-recentchanges" class="mw-list-item"><a href="/wiki/Special:RecentChanges" title="A list of recent changes to Wikipedia [r]" accesskey="r"><span>Recent changes</span></a></li><li id="n-upload" class="mw-list-item"><a href="/wiki/Wikipedia:File_upload_wizard" title="Add images or other media for use on Wikipedia"><span>Upload file</span></a></li> </ul> </div> </div> </div> </div> </div> </div> </nav> <a href="/wiki/Main_Page" class="mw-logo"> <img class="mw-logo-icon" src="/static/images/icons/wikipedia.png" alt="" aria-hidden="true" height="50" width="50"> <span class="mw-logo-container skin-invert"> <img class="mw-logo-wordmark" alt="Wikipedia" src="/static/images/mobile/copyright/wikipedia-wordmark-en.svg" style="width: 7.5em; height: 1.125em;"> <img class="mw-logo-tagline" alt="The Free Encyclopedia" src="/static/images/mobile/copyright/wikipedia-tagline-en.svg" width="117" height="13" style="width: 7.3125em; height: 0.8125em;"> </span> </a> </div> <div class="vector-header-end"> <div id="p-search" role="search" class="vector-search-box-vue vector-search-box-collapses vector-search-box-show-thumbnail vector-search-box-auto-expand-width vector-search-box"> <a href="/wiki/Special:Search" class="cdx-button cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--weight-quiet cdx-button--icon-only search-toggle" title="Search Wikipedia [f]" accesskey="f"><span class="vector-icon mw-ui-icon-search mw-ui-icon-wikimedia-search"></span> <span>Search</span> </a> <div class="vector-typeahead-search-container"> <div class="cdx-typeahead-search cdx-typeahead-search--show-thumbnail cdx-typeahead-search--auto-expand-width"> <form action="/w/index.php" id="searchform" class="cdx-search-input cdx-search-input--has-end-button"> <div id="simpleSearch" class="cdx-search-input__input-wrapper" data-search-loc="header-moved"> <div class="cdx-text-input cdx-text-input--has-start-icon"> <input class="cdx-text-input__input" type="search" name="search" placeholder="Search Wikipedia" aria-label="Search Wikipedia" autocapitalize="sentences" title="Search Wikipedia [f]" accesskey="f" id="searchInput" > <span class="cdx-text-input__icon cdx-text-input__start-icon"></span> </div> <input type="hidden" name="title" value="Special:Search"> </div> <button class="cdx-button cdx-search-input__end-button">Search</button> </form> </div> </div> </div> <nav class="vector-user-links vector-user-links-wide" aria-label="Personal tools"> <div class="vector-user-links-main"> <div id="p-vector-user-menu-preferences" class="vector-menu mw-portlet emptyPortlet" > <div class="vector-menu-content"> <ul class="vector-menu-content-list"> </ul> </div> </div> <div id="p-vector-user-menu-userpage" class="vector-menu mw-portlet emptyPortlet" > <div class="vector-menu-content"> <ul class="vector-menu-content-list"> </ul> </div> </div> <nav class="vector-appearance-landmark" aria-label="Appearance"> <div id="vector-appearance-dropdown" class="vector-dropdown " title="Change the appearance of the page's font size, width, and color" > <input type="checkbox" id="vector-appearance-dropdown-checkbox" role="button" aria-haspopup="true" data-event-name="ui.dropdown-vector-appearance-dropdown" class="vector-dropdown-checkbox " aria-label="Appearance" > <label id="vector-appearance-dropdown-label" for="vector-appearance-dropdown-checkbox" class="vector-dropdown-label cdx-button cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--weight-quiet cdx-button--icon-only " aria-hidden="true" ><span class="vector-icon mw-ui-icon-appearance mw-ui-icon-wikimedia-appearance"></span> <span class="vector-dropdown-label-text">Appearance</span> </label> <div class="vector-dropdown-content"> <div id="vector-appearance-unpinned-container" class="vector-unpinned-container"> </div> </div> </div> </nav> <div id="p-vector-user-menu-notifications" class="vector-menu mw-portlet emptyPortlet" > <div class="vector-menu-content"> <ul class="vector-menu-content-list"> </ul> </div> </div> <div id="p-vector-user-menu-overflow" class="vector-menu mw-portlet" > <div class="vector-menu-content"> <ul class="vector-menu-content-list"> <li id="pt-sitesupport-2" class="user-links-collapsible-item mw-list-item user-links-collapsible-item"><a data-mw="interface" href="https://donate.wikimedia.org/wiki/Special:FundraiserRedirector?utm_source=donate&utm_medium=sidebar&utm_campaign=C13_en.wikipedia.org&uselang=en" class=""><span>Donate</span></a> </li> <li id="pt-createaccount-2" class="user-links-collapsible-item mw-list-item user-links-collapsible-item"><a data-mw="interface" href="/w/index.php?title=Special:CreateAccount&returnto=Ensemble+learning" title="You are encouraged to create an account and log in; however, it is not mandatory" class=""><span>Create account</span></a> </li> <li id="pt-login-2" class="user-links-collapsible-item mw-list-item user-links-collapsible-item"><a data-mw="interface" href="/w/index.php?title=Special:UserLogin&returnto=Ensemble+learning" title="You're encouraged to log in; however, it's not mandatory. [o]" accesskey="o" class=""><span>Log in</span></a> </li> </ul> </div> </div> </div> <div id="vector-user-links-dropdown" class="vector-dropdown vector-user-menu vector-button-flush-right vector-user-menu-logged-out" title="Log in and more options" > <input type="checkbox" id="vector-user-links-dropdown-checkbox" role="button" aria-haspopup="true" data-event-name="ui.dropdown-vector-user-links-dropdown" class="vector-dropdown-checkbox " aria-label="Personal tools" > <label id="vector-user-links-dropdown-label" for="vector-user-links-dropdown-checkbox" class="vector-dropdown-label cdx-button cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--weight-quiet cdx-button--icon-only " aria-hidden="true" ><span class="vector-icon mw-ui-icon-ellipsis mw-ui-icon-wikimedia-ellipsis"></span> <span class="vector-dropdown-label-text">Personal tools</span> </label> <div class="vector-dropdown-content"> <div id="p-personal" class="vector-menu mw-portlet mw-portlet-personal user-links-collapsible-item" title="User menu" > <div class="vector-menu-content"> <ul class="vector-menu-content-list"> <li id="pt-sitesupport" class="user-links-collapsible-item mw-list-item"><a href="https://donate.wikimedia.org/wiki/Special:FundraiserRedirector?utm_source=donate&utm_medium=sidebar&utm_campaign=C13_en.wikipedia.org&uselang=en"><span>Donate</span></a></li><li id="pt-createaccount" class="user-links-collapsible-item mw-list-item"><a href="/w/index.php?title=Special:CreateAccount&returnto=Ensemble+learning" title="You are encouraged to create an account and log in; however, it is not mandatory"><span class="vector-icon mw-ui-icon-userAdd mw-ui-icon-wikimedia-userAdd"></span> <span>Create account</span></a></li><li id="pt-login" class="user-links-collapsible-item mw-list-item"><a href="/w/index.php?title=Special:UserLogin&returnto=Ensemble+learning" title="You're encouraged to log in; however, it's not mandatory. [o]" accesskey="o"><span class="vector-icon mw-ui-icon-logIn mw-ui-icon-wikimedia-logIn"></span> <span>Log in</span></a></li> </ul> </div> </div> <div id="p-user-menu-anon-editor" class="vector-menu mw-portlet mw-portlet-user-menu-anon-editor" > <div class="vector-menu-heading"> Pages for logged out editors <a href="/wiki/Help:Introduction" aria-label="Learn more about editing"><span>learn more</span></a> </div> <div class="vector-menu-content"> <ul class="vector-menu-content-list"> <li id="pt-anoncontribs" class="mw-list-item"><a href="/wiki/Special:MyContributions" title="A list of edits made from this IP address [y]" accesskey="y"><span>Contributions</span></a></li><li id="pt-anontalk" class="mw-list-item"><a href="/wiki/Special:MyTalk" title="Discussion about edits from this IP address [n]" accesskey="n"><span>Talk</span></a></li> </ul> </div> </div> </div> </div> </nav> </div> </header> </div> <div class="mw-page-container"> <div class="mw-page-container-inner"> <div class="vector-sitenotice-container"> <div id="siteNotice"></div> </div> <div class="vector-column-start"> <div class="vector-main-menu-container"> <div id="mw-navigation"> <nav id="mw-panel" class="vector-main-menu-landmark" aria-label="Site"> <div id="vector-main-menu-pinned-container" class="vector-pinned-container"> </div> </nav> </div> </div> <div class="vector-sticky-pinned-container"> <nav id="mw-panel-toc" aria-label="Contents" data-event-name="ui.sidebar-toc" class="mw-table-of-contents-container vector-toc-landmark"> <div id="vector-toc-pinned-container" class="vector-pinned-container"> <div id="vector-toc" class="vector-toc vector-pinnable-element"> <div class="vector-pinnable-header vector-toc-pinnable-header vector-pinnable-header-pinned" data-feature-name="toc-pinned" data-pinnable-element-id="vector-toc" > <h2 class="vector-pinnable-header-label">Contents</h2> <button class="vector-pinnable-header-toggle-button vector-pinnable-header-pin-button" data-event-name="pinnable-header.vector-toc.pin">move to sidebar</button> <button class="vector-pinnable-header-toggle-button vector-pinnable-header-unpin-button" data-event-name="pinnable-header.vector-toc.unpin">hide</button> </div> <ul class="vector-toc-contents" id="mw-panel-toc-list"> <li id="toc-mw-content-text" class="vector-toc-list-item vector-toc-level-1"> <a href="#" class="vector-toc-link"> <div class="vector-toc-text">(Top)</div> </a> </li> <li id="toc-Overview" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#Overview"> <div class="vector-toc-text"> <span class="vector-toc-numb">1</span> <span>Overview</span> </div> </a> <ul id="toc-Overview-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Ensemble_theory" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#Ensemble_theory"> <div class="vector-toc-text"> <span class="vector-toc-numb">2</span> <span>Ensemble theory</span> </div> </a> <button aria-controls="toc-Ensemble_theory-sublist" class="cdx-button cdx-button--weight-quiet cdx-button--icon-only vector-toc-toggle"> <span class="vector-icon mw-ui-icon-wikimedia-expand"></span> <span>Toggle Ensemble theory subsection</span> </button> <ul id="toc-Ensemble_theory-sublist" class="vector-toc-list"> <li id="toc-The_geometric_framework" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#The_geometric_framework"> <div class="vector-toc-text"> <span class="vector-toc-numb">2.1</span> <span>The geometric framework</span> </div> </a> <ul id="toc-The_geometric_framework-sublist" class="vector-toc-list"> </ul> </li> </ul> </li> <li id="toc-Ensemble_size" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#Ensemble_size"> <div class="vector-toc-text"> <span class="vector-toc-numb">3</span> <span>Ensemble size</span> </div> </a> <ul id="toc-Ensemble_size-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Common_types_of_ensembles" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#Common_types_of_ensembles"> <div class="vector-toc-text"> <span class="vector-toc-numb">4</span> <span>Common types of ensembles</span> </div> </a> <button aria-controls="toc-Common_types_of_ensembles-sublist" class="cdx-button cdx-button--weight-quiet cdx-button--icon-only vector-toc-toggle"> <span class="vector-icon mw-ui-icon-wikimedia-expand"></span> <span>Toggle Common types of ensembles subsection</span> </button> <ul id="toc-Common_types_of_ensembles-sublist" class="vector-toc-list"> <li id="toc-Bayes_optimal_classifier" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Bayes_optimal_classifier"> <div class="vector-toc-text"> <span class="vector-toc-numb">4.1</span> <span>Bayes optimal classifier</span> </div> </a> <ul id="toc-Bayes_optimal_classifier-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Bootstrap_aggregating_(bagging)" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Bootstrap_aggregating_(bagging)"> <div class="vector-toc-text"> <span class="vector-toc-numb">4.2</span> <span>Bootstrap aggregating (bagging)</span> </div> </a> <ul id="toc-Bootstrap_aggregating_(bagging)-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Boosting" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Boosting"> <div class="vector-toc-text"> <span class="vector-toc-numb">4.3</span> <span>Boosting</span> </div> </a> <ul id="toc-Boosting-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Bayesian_model_averaging" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Bayesian_model_averaging"> <div class="vector-toc-text"> <span class="vector-toc-numb">4.4</span> <span>Bayesian model averaging</span> </div> </a> <ul id="toc-Bayesian_model_averaging-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Bayesian_model_combination" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Bayesian_model_combination"> <div class="vector-toc-text"> <span class="vector-toc-numb">4.5</span> <span>Bayesian model combination</span> </div> </a> <ul id="toc-Bayesian_model_combination-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Bucket_of_models" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Bucket_of_models"> <div class="vector-toc-text"> <span class="vector-toc-numb">4.6</span> <span>Bucket of models</span> </div> </a> <ul id="toc-Bucket_of_models-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Amended_Cross-Entropy_Cost:_An_Approach_for_Encouraging_Diversity_in_Classification_Ensemble" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Amended_Cross-Entropy_Cost:_An_Approach_for_Encouraging_Diversity_in_Classification_Ensemble"> <div class="vector-toc-text"> <span class="vector-toc-numb">4.7</span> <span>Amended Cross-Entropy Cost: An Approach for Encouraging Diversity in Classification Ensemble</span> </div> </a> <ul id="toc-Amended_Cross-Entropy_Cost:_An_Approach_for_Encouraging_Diversity_in_Classification_Ensemble-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Stacking" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Stacking"> <div class="vector-toc-text"> <span class="vector-toc-numb">4.8</span> <span>Stacking</span> </div> </a> <ul id="toc-Stacking-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Voting" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Voting"> <div class="vector-toc-text"> <span class="vector-toc-numb">4.9</span> <span>Voting</span> </div> </a> <ul id="toc-Voting-sublist" class="vector-toc-list"> </ul> </li> </ul> </li> <li id="toc-Implementations_in_statistics_packages" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#Implementations_in_statistics_packages"> <div class="vector-toc-text"> <span class="vector-toc-numb">5</span> <span>Implementations in statistics packages</span> </div> </a> <ul id="toc-Implementations_in_statistics_packages-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Ensemble_learning_applications" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#Ensemble_learning_applications"> <div class="vector-toc-text"> <span class="vector-toc-numb">6</span> <span>Ensemble learning applications</span> </div> </a> <button aria-controls="toc-Ensemble_learning_applications-sublist" class="cdx-button cdx-button--weight-quiet cdx-button--icon-only vector-toc-toggle"> <span class="vector-icon mw-ui-icon-wikimedia-expand"></span> <span>Toggle Ensemble learning applications subsection</span> </button> <ul id="toc-Ensemble_learning_applications-sublist" class="vector-toc-list"> <li id="toc-Remote_sensing" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Remote_sensing"> <div class="vector-toc-text"> <span class="vector-toc-numb">6.1</span> <span>Remote sensing</span> </div> </a> <ul id="toc-Remote_sensing-sublist" class="vector-toc-list"> <li id="toc-Land_cover_mapping" class="vector-toc-list-item vector-toc-level-3"> <a class="vector-toc-link" href="#Land_cover_mapping"> <div class="vector-toc-text"> <span class="vector-toc-numb">6.1.1</span> <span>Land cover mapping</span> </div> </a> <ul id="toc-Land_cover_mapping-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Change_detection" class="vector-toc-list-item vector-toc-level-3"> <a class="vector-toc-link" href="#Change_detection"> <div class="vector-toc-text"> <span class="vector-toc-numb">6.1.2</span> <span>Change detection</span> </div> </a> <ul id="toc-Change_detection-sublist" class="vector-toc-list"> </ul> </li> </ul> </li> <li id="toc-Computer_security" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Computer_security"> <div class="vector-toc-text"> <span class="vector-toc-numb">6.2</span> <span>Computer security</span> </div> </a> <ul id="toc-Computer_security-sublist" class="vector-toc-list"> <li id="toc-Distributed_denial_of_service" class="vector-toc-list-item vector-toc-level-3"> <a class="vector-toc-link" href="#Distributed_denial_of_service"> <div class="vector-toc-text"> <span class="vector-toc-numb">6.2.1</span> <span>Distributed denial of service</span> </div> </a> <ul id="toc-Distributed_denial_of_service-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Malware_Detection" class="vector-toc-list-item vector-toc-level-3"> <a class="vector-toc-link" href="#Malware_Detection"> <div class="vector-toc-text"> <span class="vector-toc-numb">6.2.2</span> <span>Malware Detection</span> </div> </a> <ul id="toc-Malware_Detection-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Intrusion_detection" class="vector-toc-list-item vector-toc-level-3"> <a class="vector-toc-link" href="#Intrusion_detection"> <div class="vector-toc-text"> <span class="vector-toc-numb">6.2.3</span> <span>Intrusion detection</span> </div> </a> <ul id="toc-Intrusion_detection-sublist" class="vector-toc-list"> </ul> </li> </ul> </li> <li id="toc-Face_recognition" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Face_recognition"> <div class="vector-toc-text"> <span class="vector-toc-numb">6.3</span> <span>Face recognition</span> </div> </a> <ul id="toc-Face_recognition-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Emotion_recognition" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Emotion_recognition"> <div class="vector-toc-text"> <span class="vector-toc-numb">6.4</span> <span>Emotion recognition</span> </div> </a> <ul id="toc-Emotion_recognition-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Fraud_detection" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Fraud_detection"> <div class="vector-toc-text"> <span class="vector-toc-numb">6.5</span> <span>Fraud detection</span> </div> </a> <ul id="toc-Fraud_detection-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Financial_decision-making" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Financial_decision-making"> <div class="vector-toc-text"> <span class="vector-toc-numb">6.6</span> <span>Financial decision-making</span> </div> </a> <ul id="toc-Financial_decision-making-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Medicine" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Medicine"> <div class="vector-toc-text"> <span class="vector-toc-numb">6.7</span> <span>Medicine</span> </div> </a> <ul id="toc-Medicine-sublist" class="vector-toc-list"> </ul> </li> </ul> </li> <li id="toc-See_also" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#See_also"> <div class="vector-toc-text"> <span class="vector-toc-numb">7</span> <span>See also</span> </div> </a> <ul id="toc-See_also-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-References" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#References"> <div class="vector-toc-text"> <span class="vector-toc-numb">8</span> <span>References</span> </div> </a> <ul id="toc-References-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Further_reading" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#Further_reading"> <div class="vector-toc-text"> <span class="vector-toc-numb">9</span> <span>Further reading</span> </div> </a> <ul id="toc-Further_reading-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-External_links" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#External_links"> <div class="vector-toc-text"> <span class="vector-toc-numb">10</span> <span>External links</span> </div> </a> <ul id="toc-External_links-sublist" class="vector-toc-list"> </ul> </li> </ul> </div> </div> </nav> </div> </div> <div class="mw-content-container"> <main id="content" class="mw-body"> <header class="mw-body-header vector-page-titlebar"> <nav aria-label="Contents" class="vector-toc-landmark"> <div id="vector-page-titlebar-toc" class="vector-dropdown vector-page-titlebar-toc vector-button-flush-left" > <input type="checkbox" id="vector-page-titlebar-toc-checkbox" role="button" aria-haspopup="true" data-event-name="ui.dropdown-vector-page-titlebar-toc" class="vector-dropdown-checkbox " aria-label="Toggle the table of contents" > <label id="vector-page-titlebar-toc-label" for="vector-page-titlebar-toc-checkbox" class="vector-dropdown-label cdx-button cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--weight-quiet cdx-button--icon-only " aria-hidden="true" ><span class="vector-icon mw-ui-icon-listBullet mw-ui-icon-wikimedia-listBullet"></span> <span class="vector-dropdown-label-text">Toggle the table of contents</span> </label> <div class="vector-dropdown-content"> <div id="vector-page-titlebar-toc-unpinned-container" class="vector-unpinned-container"> </div> </div> </div> </nav> <h1 id="firstHeading" class="firstHeading mw-first-heading"><span class="mw-page-title-main">Ensemble learning</span></h1> <div id="p-lang-btn" class="vector-dropdown mw-portlet mw-portlet-lang" > <input type="checkbox" id="p-lang-btn-checkbox" role="button" aria-haspopup="true" data-event-name="ui.dropdown-p-lang-btn" class="vector-dropdown-checkbox mw-interlanguage-selector" aria-label="Go to an article in another language. Available in 15 languages" > <label id="p-lang-btn-label" for="p-lang-btn-checkbox" class="vector-dropdown-label cdx-button cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--weight-quiet cdx-button--action-progressive mw-portlet-lang-heading-15" aria-hidden="true" ><span class="vector-icon mw-ui-icon-language-progressive mw-ui-icon-wikimedia-language-progressive"></span> <span class="vector-dropdown-label-text">15 languages</span> </label> <div class="vector-dropdown-content"> <div class="vector-menu-content"> <ul class="vector-menu-content-list"> <li class="interlanguage-link interwiki-ar mw-list-item"><a href="https://ar.wikipedia.org/wiki/%D8%A7%D9%84%D8%AA%D8%B9%D9%84%D9%85_%D8%A7%D9%84%D8%AC%D9%85%D8%A7%D8%B9%D9%8A" title="التعلم الجماعي – Arabic" lang="ar" hreflang="ar" data-title="التعلم الجماعي" data-language-autonym="العربية" data-language-local-name="Arabic" class="interlanguage-link-target"><span>العربية</span></a></li><li class="interlanguage-link interwiki-ca mw-list-item"><a href="https://ca.wikipedia.org/wiki/M%C3%A8tode_de_conjunt" title="Mètode de conjunt – Catalan" lang="ca" hreflang="ca" data-title="Mètode de conjunt" data-language-autonym="Català" data-language-local-name="Catalan" class="interlanguage-link-target"><span>Català</span></a></li><li class="interlanguage-link interwiki-de mw-list-item"><a href="https://de.wikipedia.org/wiki/Ensemble_learning" title="Ensemble learning – German" lang="de" hreflang="de" data-title="Ensemble learning" data-language-autonym="Deutsch" data-language-local-name="German" class="interlanguage-link-target"><span>Deutsch</span></a></li><li class="interlanguage-link interwiki-es mw-list-item"><a href="https://es.wikipedia.org/wiki/Aprendizaje_por_conjuntos" title="Aprendizaje por conjuntos – Spanish" lang="es" hreflang="es" data-title="Aprendizaje por conjuntos" data-language-autonym="Español" data-language-local-name="Spanish" class="interlanguage-link-target"><span>Español</span></a></li><li class="interlanguage-link interwiki-fa mw-list-item"><a href="https://fa.wikipedia.org/wiki/%DB%8C%D8%A7%D8%AF%DA%AF%DB%8C%D8%B1%DB%8C_%DA%AF%D8%B1%D9%88%D9%87%DB%8C" title="یادگیری گروهی – Persian" lang="fa" hreflang="fa" data-title="یادگیری گروهی" data-language-autonym="فارسی" data-language-local-name="Persian" class="interlanguage-link-target"><span>فارسی</span></a></li><li class="interlanguage-link interwiki-fr mw-list-item"><a href="https://fr.wikipedia.org/wiki/Apprentissage_ensembliste" title="Apprentissage ensembliste – French" lang="fr" hreflang="fr" data-title="Apprentissage ensembliste" data-language-autonym="Français" data-language-local-name="French" class="interlanguage-link-target"><span>Français</span></a></li><li class="interlanguage-link interwiki-ko mw-list-item"><a href="https://ko.wikipedia.org/wiki/%EC%95%99%EC%83%81%EB%B8%94_%ED%95%99%EC%8A%B5%EB%B2%95" title="앙상블 학습법 – Korean" lang="ko" hreflang="ko" data-title="앙상블 학습법" data-language-autonym="한국어" data-language-local-name="Korean" class="interlanguage-link-target"><span>한국어</span></a></li><li class="interlanguage-link interwiki-id mw-list-item"><a href="https://id.wikipedia.org/wiki/Metode_ensemble" title="Metode ensemble – Indonesian" lang="id" hreflang="id" data-title="Metode ensemble" data-language-autonym="Bahasa Indonesia" data-language-local-name="Indonesian" class="interlanguage-link-target"><span>Bahasa Indonesia</span></a></li><li class="interlanguage-link interwiki-it mw-list-item"><a href="https://it.wikipedia.org/wiki/Apprendimento_d%27insieme" title="Apprendimento d'insieme – Italian" lang="it" hreflang="it" data-title="Apprendimento d'insieme" data-language-autonym="Italiano" data-language-local-name="Italian" class="interlanguage-link-target"><span>Italiano</span></a></li><li class="interlanguage-link interwiki-ja mw-list-item"><a href="https://ja.wikipedia.org/wiki/%E3%82%A2%E3%83%B3%E3%82%B5%E3%83%B3%E3%83%96%E3%83%AB%E3%83%BB%E3%83%A9%E3%83%BC%E3%83%8B%E3%83%B3%E3%82%B0" title="アンサンブル・ラーニング – Japanese" lang="ja" hreflang="ja" data-title="アンサンブル・ラーニング" data-language-autonym="日本語" data-language-local-name="Japanese" class="interlanguage-link-target"><span>日本語</span></a></li><li class="interlanguage-link interwiki-ru mw-list-item"><a href="https://ru.wikipedia.org/wiki/%D0%90%D0%BD%D1%81%D0%B0%D0%BC%D0%B1%D0%BB%D0%B5%D0%B2%D0%BE%D0%B5_%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D0%B5" title="Ансамблевое обучение – Russian" lang="ru" hreflang="ru" data-title="Ансамблевое обучение" data-language-autonym="Русский" data-language-local-name="Russian" class="interlanguage-link-target"><span>Русский</span></a></li><li class="interlanguage-link interwiki-sl mw-list-item"><a href="https://sl.wikipedia.org/wiki/Ansambelsko_u%C4%8Denje" title="Ansambelsko učenje – Slovenian" lang="sl" hreflang="sl" data-title="Ansambelsko učenje" data-language-autonym="Slovenščina" data-language-local-name="Slovenian" class="interlanguage-link-target"><span>Slovenščina</span></a></li><li class="interlanguage-link interwiki-uk mw-list-item"><a href="https://uk.wikipedia.org/wiki/%D0%90%D0%BD%D1%81%D0%B0%D0%BC%D0%B1%D0%BB%D0%B5%D0%B2%D0%B5_%D0%BD%D0%B0%D0%B2%D1%87%D0%B0%D0%BD%D0%BD%D1%8F" title="Ансамблеве навчання – Ukrainian" lang="uk" hreflang="uk" data-title="Ансамблеве навчання" data-language-autonym="Українська" data-language-local-name="Ukrainian" class="interlanguage-link-target"><span>Українська</span></a></li><li class="interlanguage-link interwiki-zh-yue mw-list-item"><a href="https://zh-yue.wikipedia.org/wiki/%E9%9B%86%E6%88%90%E5%AD%B8%E7%BF%92" title="集成學習 – Cantonese" lang="yue" hreflang="yue" data-title="集成學習" data-language-autonym="粵語" data-language-local-name="Cantonese" class="interlanguage-link-target"><span>粵語</span></a></li><li class="interlanguage-link interwiki-zh mw-list-item"><a href="https://zh.wikipedia.org/wiki/%E9%9B%86%E6%88%90%E5%AD%A6%E4%B9%A0" title="集成学习 – Chinese" lang="zh" hreflang="zh" data-title="集成学习" data-language-autonym="中文" data-language-local-name="Chinese" class="interlanguage-link-target"><span>中文</span></a></li> </ul> <div class="after-portlet after-portlet-lang"><span class="wb-langlinks-edit wb-langlinks-link"><a href="https://www.wikidata.org/wiki/Special:EntityPage/Q245652#sitelinks-wikipedia" title="Edit interlanguage links" class="wbc-editpage">Edit links</a></span></div> </div> </div> </div> </header> <div class="vector-page-toolbar"> <div class="vector-page-toolbar-container"> <div id="left-navigation"> <nav aria-label="Namespaces"> <div id="p-associated-pages" class="vector-menu vector-menu-tabs mw-portlet mw-portlet-associated-pages" > <div class="vector-menu-content"> <ul class="vector-menu-content-list"> <li id="ca-nstab-main" class="selected vector-tab-noicon mw-list-item"><a href="/wiki/Ensemble_learning" title="View the content page [c]" accesskey="c"><span>Article</span></a></li><li id="ca-talk" class="vector-tab-noicon mw-list-item"><a href="/wiki/Talk:Ensemble_learning" rel="discussion" title="Discuss improvements to the content page [t]" accesskey="t"><span>Talk</span></a></li> </ul> </div> </div> <div id="vector-variants-dropdown" class="vector-dropdown emptyPortlet" > <input type="checkbox" id="vector-variants-dropdown-checkbox" role="button" aria-haspopup="true" data-event-name="ui.dropdown-vector-variants-dropdown" class="vector-dropdown-checkbox " aria-label="Change language variant" > <label id="vector-variants-dropdown-label" for="vector-variants-dropdown-checkbox" class="vector-dropdown-label cdx-button cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--weight-quiet" aria-hidden="true" ><span class="vector-dropdown-label-text">English</span> </label> <div class="vector-dropdown-content"> <div id="p-variants" class="vector-menu mw-portlet mw-portlet-variants emptyPortlet" > <div class="vector-menu-content"> <ul class="vector-menu-content-list"> </ul> </div> </div> </div> </div> </nav> </div> <div id="right-navigation" class="vector-collapsible"> <nav aria-label="Views"> <div id="p-views" class="vector-menu vector-menu-tabs mw-portlet mw-portlet-views" > <div class="vector-menu-content"> <ul class="vector-menu-content-list"> <li id="ca-view" class="selected vector-tab-noicon mw-list-item"><a href="/wiki/Ensemble_learning"><span>Read</span></a></li><li id="ca-edit" class="vector-tab-noicon mw-list-item"><a href="/w/index.php?title=Ensemble_learning&action=edit" title="Edit this page [e]" accesskey="e"><span>Edit</span></a></li><li id="ca-history" class="vector-tab-noicon mw-list-item"><a href="/w/index.php?title=Ensemble_learning&action=history" title="Past revisions of this page [h]" accesskey="h"><span>View history</span></a></li> </ul> </div> </div> </nav> <nav class="vector-page-tools-landmark" aria-label="Page tools"> <div id="vector-page-tools-dropdown" class="vector-dropdown vector-page-tools-dropdown" > <input type="checkbox" id="vector-page-tools-dropdown-checkbox" role="button" aria-haspopup="true" data-event-name="ui.dropdown-vector-page-tools-dropdown" class="vector-dropdown-checkbox " aria-label="Tools" > <label id="vector-page-tools-dropdown-label" for="vector-page-tools-dropdown-checkbox" class="vector-dropdown-label cdx-button cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--weight-quiet" aria-hidden="true" ><span class="vector-dropdown-label-text">Tools</span> </label> <div class="vector-dropdown-content"> <div id="vector-page-tools-unpinned-container" class="vector-unpinned-container"> <div id="vector-page-tools" class="vector-page-tools vector-pinnable-element"> <div class="vector-pinnable-header vector-page-tools-pinnable-header vector-pinnable-header-unpinned" data-feature-name="page-tools-pinned" data-pinnable-element-id="vector-page-tools" data-pinned-container-id="vector-page-tools-pinned-container" data-unpinned-container-id="vector-page-tools-unpinned-container" > <div class="vector-pinnable-header-label">Tools</div> <button class="vector-pinnable-header-toggle-button vector-pinnable-header-pin-button" data-event-name="pinnable-header.vector-page-tools.pin">move to sidebar</button> <button class="vector-pinnable-header-toggle-button vector-pinnable-header-unpin-button" data-event-name="pinnable-header.vector-page-tools.unpin">hide</button> </div> <div id="p-cactions" class="vector-menu mw-portlet mw-portlet-cactions emptyPortlet vector-has-collapsible-items" title="More options" > <div class="vector-menu-heading"> Actions </div> <div class="vector-menu-content"> <ul class="vector-menu-content-list"> <li id="ca-more-view" class="selected vector-more-collapsible-item mw-list-item"><a href="/wiki/Ensemble_learning"><span>Read</span></a></li><li id="ca-more-edit" class="vector-more-collapsible-item mw-list-item"><a href="/w/index.php?title=Ensemble_learning&action=edit" title="Edit this page [e]" accesskey="e"><span>Edit</span></a></li><li id="ca-more-history" class="vector-more-collapsible-item mw-list-item"><a href="/w/index.php?title=Ensemble_learning&action=history"><span>View history</span></a></li> </ul> </div> </div> <div id="p-tb" class="vector-menu mw-portlet mw-portlet-tb" > <div class="vector-menu-heading"> General </div> <div class="vector-menu-content"> <ul class="vector-menu-content-list"> <li id="t-whatlinkshere" class="mw-list-item"><a href="/wiki/Special:WhatLinksHere/Ensemble_learning" title="List of all English Wikipedia pages containing links to this page [j]" accesskey="j"><span>What links here</span></a></li><li id="t-recentchangeslinked" class="mw-list-item"><a href="/wiki/Special:RecentChangesLinked/Ensemble_learning" rel="nofollow" title="Recent changes in pages linked from this page [k]" accesskey="k"><span>Related changes</span></a></li><li id="t-upload" class="mw-list-item"><a href="/wiki/Wikipedia:File_Upload_Wizard" title="Upload files [u]" accesskey="u"><span>Upload file</span></a></li><li id="t-specialpages" class="mw-list-item"><a href="/wiki/Special:SpecialPages" title="A list of all special pages [q]" accesskey="q"><span>Special pages</span></a></li><li id="t-permalink" class="mw-list-item"><a href="/w/index.php?title=Ensemble_learning&oldid=1254901523" title="Permanent link to this revision of this page"><span>Permanent link</span></a></li><li id="t-info" class="mw-list-item"><a href="/w/index.php?title=Ensemble_learning&action=info" title="More information about this page"><span>Page information</span></a></li><li id="t-cite" class="mw-list-item"><a href="/w/index.php?title=Special:CiteThisPage&page=Ensemble_learning&id=1254901523&wpFormIdentifier=titleform" title="Information on how to cite this page"><span>Cite this page</span></a></li><li id="t-urlshortener" class="mw-list-item"><a href="/w/index.php?title=Special:UrlShortener&url=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FEnsemble_learning"><span>Get shortened URL</span></a></li><li id="t-urlshortener-qrcode" class="mw-list-item"><a href="/w/index.php?title=Special:QrCode&url=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FEnsemble_learning"><span>Download QR code</span></a></li> </ul> </div> </div> <div id="p-coll-print_export" class="vector-menu mw-portlet mw-portlet-coll-print_export" > <div class="vector-menu-heading"> Print/export </div> <div class="vector-menu-content"> <ul class="vector-menu-content-list"> <li id="coll-download-as-rl" class="mw-list-item"><a href="/w/index.php?title=Special:DownloadAsPdf&page=Ensemble_learning&action=show-download-screen" title="Download this page as a PDF file"><span>Download as PDF</span></a></li><li id="t-print" class="mw-list-item"><a href="/w/index.php?title=Ensemble_learning&printable=yes" title="Printable version of this page [p]" accesskey="p"><span>Printable version</span></a></li> </ul> </div> </div> <div id="p-wikibase-otherprojects" class="vector-menu mw-portlet mw-portlet-wikibase-otherprojects" > <div class="vector-menu-heading"> In other projects </div> <div class="vector-menu-content"> <ul class="vector-menu-content-list"> <li id="t-wikibase" class="wb-otherproject-link wb-otherproject-wikibase-dataitem mw-list-item"><a href="https://www.wikidata.org/wiki/Special:EntityPage/Q245652" title="Structured data on this page hosted by Wikidata [g]" accesskey="g"><span>Wikidata item</span></a></li> </ul> </div> </div> </div> </div> </div> </div> </nav> </div> </div> </div> <div class="vector-column-end"> <div class="vector-sticky-pinned-container"> <nav class="vector-page-tools-landmark" aria-label="Page tools"> <div id="vector-page-tools-pinned-container" class="vector-pinned-container"> </div> </nav> <nav class="vector-appearance-landmark" aria-label="Appearance"> <div id="vector-appearance-pinned-container" class="vector-pinned-container"> <div id="vector-appearance" class="vector-appearance vector-pinnable-element"> <div class="vector-pinnable-header vector-appearance-pinnable-header vector-pinnable-header-pinned" data-feature-name="appearance-pinned" data-pinnable-element-id="vector-appearance" data-pinned-container-id="vector-appearance-pinned-container" data-unpinned-container-id="vector-appearance-unpinned-container" > <div class="vector-pinnable-header-label">Appearance</div> <button class="vector-pinnable-header-toggle-button vector-pinnable-header-pin-button" data-event-name="pinnable-header.vector-appearance.pin">move to sidebar</button> <button class="vector-pinnable-header-toggle-button vector-pinnable-header-unpin-button" data-event-name="pinnable-header.vector-appearance.unpin">hide</button> </div> </div> </div> </nav> </div> </div> <div id="bodyContent" class="vector-body" aria-labelledby="firstHeading" data-mw-ve-target-container> <div class="vector-body-before-content"> <div class="mw-indicators"> </div> <div id="siteSub" class="noprint">From Wikipedia, the free encyclopedia</div> </div> <div id="contentSub"><div id="mw-content-subtitle"></div></div> <div id="mw-content-text" class="mw-body-content"><div class="mw-content-ltr mw-parser-output" lang="en" dir="ltr"><div class="shortdescription nomobile noexcerpt noprint searchaux" style="display:none">Statistics and machine learning technique</div> <style data-mw-deduplicate="TemplateStyles:r1244144826">.mw-parser-output .machine-learning-list-title{background-color:#ddddff}html.skin-theme-clientpref-night .mw-parser-output .machine-learning-list-title{background-color:#222}@media(prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .machine-learning-list-title{background-color:#222}}</style> <style data-mw-deduplicate="TemplateStyles:r1129693374">.mw-parser-output .hlist dl,.mw-parser-output .hlist ol,.mw-parser-output .hlist ul{margin:0;padding:0}.mw-parser-output .hlist dd,.mw-parser-output .hlist dt,.mw-parser-output .hlist li{margin:0;display:inline}.mw-parser-output .hlist.inline,.mw-parser-output .hlist.inline dl,.mw-parser-output .hlist.inline ol,.mw-parser-output .hlist.inline ul,.mw-parser-output .hlist dl dl,.mw-parser-output .hlist dl ol,.mw-parser-output .hlist dl ul,.mw-parser-output .hlist ol dl,.mw-parser-output .hlist ol ol,.mw-parser-output .hlist ol ul,.mw-parser-output .hlist ul dl,.mw-parser-output .hlist ul ol,.mw-parser-output .hlist ul ul{display:inline}.mw-parser-output .hlist .mw-empty-li{display:none}.mw-parser-output .hlist dt::after{content:": "}.mw-parser-output .hlist dd::after,.mw-parser-output .hlist li::after{content:" · ";font-weight:bold}.mw-parser-output .hlist dd:last-child::after,.mw-parser-output .hlist dt:last-child::after,.mw-parser-output .hlist li:last-child::after{content:none}.mw-parser-output .hlist dd dd:first-child::before,.mw-parser-output .hlist dd dt:first-child::before,.mw-parser-output .hlist dd li:first-child::before,.mw-parser-output .hlist dt dd:first-child::before,.mw-parser-output .hlist dt dt:first-child::before,.mw-parser-output .hlist dt li:first-child::before,.mw-parser-output .hlist li dd:first-child::before,.mw-parser-output .hlist li dt:first-child::before,.mw-parser-output .hlist li li:first-child::before{content:" (";font-weight:normal}.mw-parser-output .hlist dd dd:last-child::after,.mw-parser-output .hlist dd dt:last-child::after,.mw-parser-output .hlist dd li:last-child::after,.mw-parser-output .hlist dt dd:last-child::after,.mw-parser-output .hlist dt dt:last-child::after,.mw-parser-output .hlist dt li:last-child::after,.mw-parser-output .hlist li dd:last-child::after,.mw-parser-output .hlist li dt:last-child::after,.mw-parser-output .hlist li li:last-child::after{content:")";font-weight:normal}.mw-parser-output .hlist ol{counter-reset:listitem}.mw-parser-output .hlist ol>li{counter-increment:listitem}.mw-parser-output .hlist ol>li::before{content:" "counter(listitem)"\a0 "}.mw-parser-output .hlist dd ol>li:first-child::before,.mw-parser-output .hlist dt ol>li:first-child::before,.mw-parser-output .hlist li ol>li:first-child::before{content:" ("counter(listitem)"\a0 "}</style><style data-mw-deduplicate="TemplateStyles:r1246091330">.mw-parser-output .sidebar{width:22em;float:right;clear:right;margin:0.5em 0 1em 1em;background:var(--background-color-neutral-subtle,#f8f9fa);border:1px solid var(--border-color-base,#a2a9b1);padding:0.2em;text-align:center;line-height:1.4em;font-size:88%;border-collapse:collapse;display:table}body.skin-minerva .mw-parser-output .sidebar{display:table!important;float:right!important;margin:0.5em 0 1em 1em!important}.mw-parser-output .sidebar-subgroup{width:100%;margin:0;border-spacing:0}.mw-parser-output .sidebar-left{float:left;clear:left;margin:0.5em 1em 1em 0}.mw-parser-output .sidebar-none{float:none;clear:both;margin:0.5em 1em 1em 0}.mw-parser-output .sidebar-outer-title{padding:0 0.4em 0.2em;font-size:125%;line-height:1.2em;font-weight:bold}.mw-parser-output .sidebar-top-image{padding:0.4em}.mw-parser-output .sidebar-top-caption,.mw-parser-output .sidebar-pretitle-with-top-image,.mw-parser-output .sidebar-caption{padding:0.2em 0.4em 0;line-height:1.2em}.mw-parser-output .sidebar-pretitle{padding:0.4em 0.4em 0;line-height:1.2em}.mw-parser-output .sidebar-title,.mw-parser-output .sidebar-title-with-pretitle{padding:0.2em 0.8em;font-size:145%;line-height:1.2em}.mw-parser-output .sidebar-title-with-pretitle{padding:0.1em 0.4em}.mw-parser-output .sidebar-image{padding:0.2em 0.4em 0.4em}.mw-parser-output .sidebar-heading{padding:0.1em 0.4em}.mw-parser-output .sidebar-content{padding:0 0.5em 0.4em}.mw-parser-output .sidebar-content-with-subgroup{padding:0.1em 0.4em 0.2em}.mw-parser-output .sidebar-above,.mw-parser-output .sidebar-below{padding:0.3em 0.8em;font-weight:bold}.mw-parser-output .sidebar-collapse .sidebar-above,.mw-parser-output .sidebar-collapse .sidebar-below{border-top:1px solid #aaa;border-bottom:1px solid #aaa}.mw-parser-output .sidebar-navbar{text-align:right;font-size:115%;padding:0 0.4em 0.4em}.mw-parser-output .sidebar-list-title{padding:0 0.4em;text-align:left;font-weight:bold;line-height:1.6em;font-size:105%}.mw-parser-output .sidebar-list-title-c{padding:0 0.4em;text-align:center;margin:0 3.3em}@media(max-width:640px){body.mediawiki .mw-parser-output .sidebar{width:100%!important;clear:both;float:none!important;margin-left:0!important;margin-right:0!important}}body.skin--responsive .mw-parser-output .sidebar a>img{max-width:none!important}@media screen{html.skin-theme-clientpref-night .mw-parser-output .sidebar:not(.notheme) .sidebar-list-title,html.skin-theme-clientpref-night .mw-parser-output .sidebar:not(.notheme) .sidebar-title-with-pretitle{background:transparent!important}html.skin-theme-clientpref-night .mw-parser-output .sidebar:not(.notheme) .sidebar-title-with-pretitle a{color:var(--color-progressive)!important}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .sidebar:not(.notheme) .sidebar-list-title,html.skin-theme-clientpref-os .mw-parser-output .sidebar:not(.notheme) .sidebar-title-with-pretitle{background:transparent!important}html.skin-theme-clientpref-os .mw-parser-output .sidebar:not(.notheme) .sidebar-title-with-pretitle a{color:var(--color-progressive)!important}}@media print{body.ns-0 .mw-parser-output .sidebar{display:none!important}}</style><style data-mw-deduplicate="TemplateStyles:r886047488">.mw-parser-output .nobold{font-weight:normal}</style><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r886047488"><table class="sidebar sidebar-collapse nomobile nowraplinks"><tbody><tr><td class="sidebar-pretitle">Part of a series on</td></tr><tr><th class="sidebar-title-with-pretitle"><a href="/wiki/Machine_learning" title="Machine learning">Machine learning</a><br />and <a href="/wiki/Data_mining" title="Data mining">data mining</a></th></tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)">Paradigms</div><div class="sidebar-list-content mw-collapsible-content hlist"> <ul><li><a href="/wiki/Supervised_learning" title="Supervised learning">Supervised learning</a></li> <li><a href="/wiki/Unsupervised_learning" title="Unsupervised learning">Unsupervised learning</a></li> <li><a href="/wiki/Semi-supervised_learning" class="mw-redirect" title="Semi-supervised learning">Semi-supervised learning</a></li> <li><a href="/wiki/Self-supervised_learning" title="Self-supervised learning">Self-supervised learning</a></li> <li><a href="/wiki/Reinforcement_learning" title="Reinforcement learning">Reinforcement learning</a></li> <li><a href="/wiki/Meta-learning_(computer_science)" title="Meta-learning (computer science)">Meta-learning</a></li> <li><a href="/wiki/Online_machine_learning" title="Online machine learning">Online learning</a></li> <li><a href="/wiki/Batch_learning" class="mw-redirect" title="Batch learning">Batch learning</a></li> <li><a href="/wiki/Curriculum_learning" title="Curriculum learning">Curriculum learning</a></li> <li><a href="/wiki/Rule-based_machine_learning" title="Rule-based machine learning">Rule-based learning</a></li> <li><a href="/wiki/Neuro-symbolic_AI" title="Neuro-symbolic AI">Neuro-symbolic AI</a></li> <li><a href="/wiki/Neuromorphic_engineering" class="mw-redirect" title="Neuromorphic engineering">Neuromorphic engineering</a></li> <li><a href="/wiki/Quantum_machine_learning" title="Quantum machine learning">Quantum machine learning</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)">Problems</div><div class="sidebar-list-content mw-collapsible-content hlist"> <ul><li><a href="/wiki/Statistical_classification" title="Statistical classification">Classification</a></li> <li><a href="/wiki/Generative_model" title="Generative model">Generative modeling</a></li> <li><a href="/wiki/Regression_analysis" title="Regression analysis">Regression</a></li> <li><a href="/wiki/Cluster_analysis" title="Cluster analysis">Clustering</a></li> <li><a href="/wiki/Dimensionality_reduction" title="Dimensionality reduction">Dimensionality reduction</a></li> <li><a href="/wiki/Density_estimation" title="Density estimation">Density estimation</a></li> <li><a href="/wiki/Anomaly_detection" title="Anomaly detection">Anomaly detection</a></li> <li><a href="/wiki/Data_cleaning" class="mw-redirect" title="Data cleaning">Data cleaning</a></li> <li><a href="/wiki/Automated_machine_learning" title="Automated machine learning">AutoML</a></li> <li><a href="/wiki/Association_rule_learning" title="Association rule learning">Association rules</a></li> <li><a href="/wiki/Semantic_analysis_(machine_learning)" title="Semantic analysis (machine learning)">Semantic analysis</a></li> <li><a href="/wiki/Structured_prediction" title="Structured prediction">Structured prediction</a></li> <li><a href="/wiki/Feature_engineering" title="Feature engineering">Feature engineering</a></li> <li><a href="/wiki/Feature_learning" title="Feature learning">Feature learning</a></li> <li><a href="/wiki/Learning_to_rank" title="Learning to rank">Learning to rank</a></li> <li><a href="/wiki/Grammar_induction" title="Grammar induction">Grammar induction</a></li> <li><a href="/wiki/Ontology_learning" title="Ontology learning">Ontology learning</a></li> <li><a href="/wiki/Multimodal_learning" title="Multimodal learning">Multimodal learning</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><div style="display: inline-block; line-height: 1.2em; padding: .1em 0;"><a href="/wiki/Supervised_learning" title="Supervised learning">Supervised learning</a><br /><span class="nobold"><span style="font-size:85%;">(<b><a href="/wiki/Statistical_classification" title="Statistical classification">classification</a></b> • <b><a href="/wiki/Regression_analysis" title="Regression analysis">regression</a></b>)</span></span> </div></div><div class="sidebar-list-content mw-collapsible-content hlist"> <ul><li><a href="/wiki/Apprenticeship_learning" title="Apprenticeship learning">Apprenticeship learning</a></li> <li><a href="/wiki/Decision_tree_learning" title="Decision tree learning">Decision trees</a></li> <li><a class="mw-selflink selflink">Ensembles</a> <ul><li><a href="/wiki/Bootstrap_aggregating" title="Bootstrap aggregating">Bagging</a></li> <li><a href="/wiki/Boosting_(machine_learning)" title="Boosting (machine learning)">Boosting</a></li> <li><a href="/wiki/Random_forest" title="Random forest">Random forest</a></li></ul></li> <li><a href="/wiki/K-nearest_neighbors_algorithm" title="K-nearest neighbors algorithm"><i>k</i>-NN</a></li> <li><a href="/wiki/Linear_regression" title="Linear regression">Linear regression</a></li> <li><a href="/wiki/Naive_Bayes_classifier" title="Naive Bayes classifier">Naive Bayes</a></li> <li><a href="/wiki/Artificial_neural_network" class="mw-redirect" title="Artificial neural network">Artificial neural networks</a></li> <li><a href="/wiki/Logistic_regression" title="Logistic regression">Logistic regression</a></li> <li><a href="/wiki/Perceptron" title="Perceptron">Perceptron</a></li> <li><a href="/wiki/Relevance_vector_machine" title="Relevance vector machine">Relevance vector machine (RVM)</a></li> <li><a href="/wiki/Support_vector_machine" title="Support vector machine">Support vector machine (SVM)</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="/wiki/Cluster_analysis" title="Cluster analysis">Clustering</a></div><div class="sidebar-list-content mw-collapsible-content hlist"> <ul><li><a href="/wiki/BIRCH" title="BIRCH">BIRCH</a></li> <li><a href="/wiki/CURE_algorithm" title="CURE algorithm">CURE</a></li> <li><a href="/wiki/Hierarchical_clustering" title="Hierarchical clustering">Hierarchical</a></li> <li><a href="/wiki/K-means_clustering" title="K-means clustering"><i>k</i>-means</a></li> <li><a href="/wiki/Fuzzy_clustering" title="Fuzzy clustering">Fuzzy</a></li> <li><a href="/wiki/Expectation%E2%80%93maximization_algorithm" title="Expectation–maximization algorithm">Expectation–maximization (EM)</a></li> <li><br /><a href="/wiki/DBSCAN" title="DBSCAN">DBSCAN</a></li> <li><a href="/wiki/OPTICS_algorithm" title="OPTICS algorithm">OPTICS</a></li> <li><a href="/wiki/Mean_shift" title="Mean shift">Mean shift</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="/wiki/Dimensionality_reduction" title="Dimensionality reduction">Dimensionality reduction</a></div><div class="sidebar-list-content mw-collapsible-content hlist"> <ul><li><a href="/wiki/Factor_analysis" title="Factor analysis">Factor analysis</a></li> <li><a href="/wiki/Canonical_correlation" title="Canonical correlation">CCA</a></li> <li><a href="/wiki/Independent_component_analysis" title="Independent component analysis">ICA</a></li> <li><a href="/wiki/Linear_discriminant_analysis" title="Linear discriminant analysis">LDA</a></li> <li><a href="/wiki/Non-negative_matrix_factorization" title="Non-negative matrix factorization">NMF</a></li> <li><a href="/wiki/Principal_component_analysis" title="Principal component analysis">PCA</a></li> <li><a href="/wiki/Proper_generalized_decomposition" title="Proper generalized decomposition">PGD</a></li> <li><a href="/wiki/T-distributed_stochastic_neighbor_embedding" title="T-distributed stochastic neighbor embedding">t-SNE</a></li> <li><a href="/wiki/Sparse_dictionary_learning" title="Sparse dictionary learning">SDL</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="/wiki/Structured_prediction" title="Structured prediction">Structured prediction</a></div><div class="sidebar-list-content mw-collapsible-content hlist"> <ul><li><a href="/wiki/Graphical_model" title="Graphical model">Graphical models</a> <ul><li><a href="/wiki/Bayesian_network" title="Bayesian network">Bayes net</a></li> <li><a href="/wiki/Conditional_random_field" title="Conditional random field">Conditional random field</a></li> <li><a href="/wiki/Hidden_Markov_model" title="Hidden Markov model">Hidden Markov</a></li></ul></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="/wiki/Anomaly_detection" title="Anomaly detection">Anomaly detection</a></div><div class="sidebar-list-content mw-collapsible-content hlist"> <ul><li><a href="/wiki/Random_sample_consensus" title="Random sample consensus">RANSAC</a></li> <li><a href="/wiki/K-nearest_neighbors_algorithm" title="K-nearest neighbors algorithm"><i>k</i>-NN</a></li> <li><a href="/wiki/Local_outlier_factor" title="Local outlier factor">Local outlier factor</a></li> <li><a href="/wiki/Isolation_forest" title="Isolation forest">Isolation forest</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="/wiki/Artificial_neural_network" class="mw-redirect" title="Artificial neural network">Artificial neural network</a></div><div class="sidebar-list-content mw-collapsible-content hlist"> <ul><li><a href="/wiki/Autoencoder" title="Autoencoder">Autoencoder</a></li> <li><a href="/wiki/Deep_learning" title="Deep learning">Deep learning</a></li> <li><a href="/wiki/Feedforward_neural_network" title="Feedforward neural network">Feedforward neural network</a></li> <li><a href="/wiki/Recurrent_neural_network" title="Recurrent neural network">Recurrent neural network</a> <ul><li><a href="/wiki/Long_short-term_memory" title="Long short-term memory">LSTM</a></li> <li><a href="/wiki/Gated_recurrent_unit" title="Gated recurrent unit">GRU</a></li> <li><a href="/wiki/Echo_state_network" title="Echo state network">ESN</a></li> <li><a href="/wiki/Reservoir_computing" title="Reservoir computing">reservoir computing</a></li></ul></li> <li><a href="/wiki/Boltzmann_machine" title="Boltzmann machine">Boltzmann machine</a> <ul><li><a href="/wiki/Restricted_Boltzmann_machine" title="Restricted Boltzmann machine">Restricted</a></li></ul></li> <li><a href="/wiki/Generative_adversarial_network" title="Generative adversarial network">GAN</a></li> <li><a href="/wiki/Diffusion_model" title="Diffusion model">Diffusion model</a></li> <li><a href="/wiki/Self-organizing_map" title="Self-organizing map">SOM</a></li> <li><a href="/wiki/Convolutional_neural_network" title="Convolutional neural network">Convolutional neural network</a> <ul><li><a href="/wiki/U-Net" title="U-Net">U-Net</a></li> <li><a href="/wiki/LeNet" title="LeNet">LeNet</a></li> <li><a href="/wiki/AlexNet" title="AlexNet">AlexNet</a></li> <li><a href="/wiki/DeepDream" title="DeepDream">DeepDream</a></li></ul></li> <li><a href="/wiki/Neural_radiance_field" title="Neural radiance field">Neural radiance field</a></li> <li><a href="/wiki/Transformer_(machine_learning_model)" class="mw-redirect" title="Transformer (machine learning model)">Transformer</a> <ul><li><a href="/wiki/Vision_transformer" title="Vision transformer">Vision</a></li></ul></li> <li><a href="/wiki/Mamba_(deep_learning_architecture)" title="Mamba (deep learning architecture)">Mamba</a></li> <li><a href="/wiki/Spiking_neural_network" title="Spiking neural network">Spiking neural network</a></li> <li><a href="/wiki/Memtransistor" title="Memtransistor">Memtransistor</a></li> <li><a href="/wiki/Electrochemical_RAM" title="Electrochemical RAM">Electrochemical RAM</a> (ECRAM)</li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="/wiki/Reinforcement_learning" title="Reinforcement learning">Reinforcement learning</a></div><div class="sidebar-list-content mw-collapsible-content hlist"> <ul><li><a href="/wiki/Q-learning" title="Q-learning">Q-learning</a></li> <li><a href="/wiki/State%E2%80%93action%E2%80%93reward%E2%80%93state%E2%80%93action" title="State–action–reward–state–action">SARSA</a></li> <li><a href="/wiki/Temporal_difference_learning" title="Temporal difference learning">Temporal difference (TD)</a></li> <li><a href="/wiki/Multi-agent_reinforcement_learning" title="Multi-agent reinforcement learning">Multi-agent</a> <ul><li><a href="/wiki/Self-play_(reinforcement_learning_technique)" class="mw-redirect" title="Self-play (reinforcement learning technique)">Self-play</a></li></ul></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)">Learning with humans</div><div class="sidebar-list-content mw-collapsible-content hlist"> <ul><li><a href="/wiki/Active_learning_(machine_learning)" title="Active learning (machine learning)">Active learning</a></li> <li><a href="/wiki/Crowdsourcing" title="Crowdsourcing">Crowdsourcing</a></li> <li><a href="/wiki/Human-in-the-loop" title="Human-in-the-loop">Human-in-the-loop</a></li> <li><a href="/wiki/Reinforcement_learning_from_human_feedback" title="Reinforcement learning from human feedback">RLHF</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)">Model diagnostics</div><div class="sidebar-list-content mw-collapsible-content hlist"> <ul><li><a href="/wiki/Coefficient_of_determination" title="Coefficient of determination">Coefficient of determination</a></li> <li><a href="/wiki/Confusion_matrix" title="Confusion matrix">Confusion matrix</a></li> <li><a href="/wiki/Learning_curve_(machine_learning)" title="Learning curve (machine learning)">Learning curve</a></li> <li><a href="/wiki/Receiver_operating_characteristic" title="Receiver operating characteristic">ROC curve</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)">Mathematical foundations</div><div class="sidebar-list-content mw-collapsible-content hlist"> <ul><li><a href="/wiki/Kernel_machines" class="mw-redirect" title="Kernel machines">Kernel machines</a></li> <li><a href="/wiki/Bias%E2%80%93variance_tradeoff" title="Bias–variance tradeoff">Bias–variance tradeoff</a></li> <li><a href="/wiki/Computational_learning_theory" title="Computational learning theory">Computational learning theory</a></li> <li><a href="/wiki/Empirical_risk_minimization" title="Empirical risk minimization">Empirical risk minimization</a></li> <li><a href="/wiki/Occam_learning" title="Occam learning">Occam learning</a></li> <li><a href="/wiki/Probably_approximately_correct_learning" title="Probably approximately correct learning">PAC learning</a></li> <li><a href="/wiki/Statistical_learning_theory" title="Statistical learning theory">Statistical learning</a></li> <li><a href="/wiki/Vapnik%E2%80%93Chervonenkis_theory" title="Vapnik–Chervonenkis theory">VC theory</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)">Journals and conferences</div><div class="sidebar-list-content mw-collapsible-content hlist"> <ul><li><a href="/wiki/ECML_PKDD" title="ECML PKDD">ECML PKDD</a></li> <li><a href="/wiki/Conference_on_Neural_Information_Processing_Systems" title="Conference on Neural Information Processing Systems">NeurIPS</a></li> <li><a href="/wiki/International_Conference_on_Machine_Learning" title="International Conference on Machine Learning">ICML</a></li> <li><a href="/wiki/International_Conference_on_Learning_Representations" title="International Conference on Learning Representations">ICLR</a></li> <li><a href="/wiki/International_Joint_Conference_on_Artificial_Intelligence" title="International Joint Conference on Artificial Intelligence">IJCAI</a></li> <li><a href="/wiki/Machine_Learning_(journal)" title="Machine Learning (journal)">ML</a></li> <li><a href="/wiki/Journal_of_Machine_Learning_Research" title="Journal of Machine Learning Research">JMLR</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)">Related articles</div><div class="sidebar-list-content mw-collapsible-content hlist"> <ul><li><a href="/wiki/Glossary_of_artificial_intelligence" title="Glossary of artificial intelligence">Glossary of artificial intelligence</a></li> <li><a href="/wiki/List_of_datasets_for_machine-learning_research" title="List of datasets for machine-learning research">List of datasets for machine-learning research</a> <ul><li><a href="/wiki/List_of_datasets_in_computer_vision_and_image_processing" title="List of datasets in computer vision and image processing">List of datasets in computer vision and image processing</a></li></ul></li> <li><a href="/wiki/Outline_of_machine_learning" title="Outline of machine learning">Outline of machine learning</a></li></ul></div></div></td> </tr><tr><td class="sidebar-navbar"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1129693374"><style data-mw-deduplicate="TemplateStyles:r1239400231">.mw-parser-output .navbar{display:inline;font-size:88%;font-weight:normal}.mw-parser-output .navbar-collapse{float:left;text-align:left}.mw-parser-output .navbar-boxtext{word-spacing:0}.mw-parser-output .navbar ul{display:inline-block;white-space:nowrap;line-height:inherit}.mw-parser-output .navbar-brackets::before{margin-right:-0.125em;content:"[ "}.mw-parser-output .navbar-brackets::after{margin-left:-0.125em;content:" ]"}.mw-parser-output .navbar li{word-spacing:-0.125em}.mw-parser-output .navbar a>span,.mw-parser-output .navbar a>abbr{text-decoration:inherit}.mw-parser-output .navbar-mini abbr{font-variant:small-caps;border-bottom:none;text-decoration:none;cursor:inherit}.mw-parser-output .navbar-ct-full{font-size:114%;margin:0 7em}.mw-parser-output .navbar-ct-mini{font-size:114%;margin:0 4em}html.skin-theme-clientpref-night .mw-parser-output .navbar li a abbr{color:var(--color-base)!important}@media(prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .navbar li a abbr{color:var(--color-base)!important}}@media print{.mw-parser-output .navbar{display:none!important}}</style><div class="navbar plainlinks hlist navbar-mini"><ul><li class="nv-view"><a href="/wiki/Template:Machine_learning" title="Template:Machine learning"><abbr title="View this template">v</abbr></a></li><li class="nv-talk"><a href="/wiki/Template_talk:Machine_learning" title="Template talk:Machine learning"><abbr title="Discuss this template">t</abbr></a></li><li class="nv-edit"><a href="/wiki/Special:EditPage/Template:Machine_learning" title="Special:EditPage/Template:Machine learning"><abbr title="Edit this template">e</abbr></a></li></ul></div></td></tr></tbody></table> <p>In <a href="/wiki/Statistics" title="Statistics">statistics</a> and <a href="/wiki/Machine_learning" title="Machine learning">machine learning</a>, <b>ensemble methods</b> use multiple learning algorithms to obtain better <a href="/wiki/Predictive_inference" class="mw-redirect" title="Predictive inference">predictive performance</a> than could be obtained from any of the constituent learning algorithms alone.<sup id="cite_ref-1" class="reference"><a href="#cite_note-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-2" class="reference"><a href="#cite_note-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-Rokach2010_3-0" class="reference"><a href="#cite_note-Rokach2010-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup> Unlike a <a href="/wiki/Statistical_ensemble" class="mw-redirect" title="Statistical ensemble">statistical ensemble</a> in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives. </p> <meta property="mw:PageProp/toc" /> <div class="mw-heading mw-heading2"><h2 id="Overview">Overview</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=1" title="Edit section: Overview"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p><a href="/wiki/Supervised_learning" title="Supervised learning">Supervised learning</a> algorithms search through a <a href="/wiki/Hypothesis" title="Hypothesis">hypothesis</a> space to find a suitable hypothesis that will make good predictions with a particular problem.<sup id="cite_ref-4" class="reference"><a href="#cite_note-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> Even if this space contains hypotheses that are very well-suited for a particular problem, it may be very difficult to find a good one. Ensembles combine multiple hypotheses to form one which should be theoretically better. </p><p><i>Ensemble learning</i> trains two or more machine learning algorithms on a specific <a href="/wiki/Classification" title="Classification">classification</a> or <a href="/wiki/Regression_analysis" title="Regression analysis">regression</a> task. The algorithms within the ensemble model are generally referred as "base models", "base learners", or "weak learners" in literature. These base models can be constructed using a single modelling algorithm, or several different algorithms. The idea is to train a diverse set of weak models on the same modelling task, such that the outputs of each weak learner have poor predictive ability (i.e., high <a href="/wiki/Bias_(statistics)" title="Bias (statistics)">bias</a>), and among all weak learners, the outcome and error values exhibit high <a href="/wiki/Variance" title="Variance">variance</a>. Fundamentally, an ensemble learning model trains at least two high-bias (weak) and high-variance (diverse) models to be combined into a better-performing model. The set of weak models — which would not produce satisfactory predictive results individually — are combined or averaged to produce a single, high performing, accurate, and low-variance model to fit the task as required. </p><p>Ensemble learning typically refers to bagging (<a href="/wiki/Bootstrap_aggregating" title="Bootstrap aggregating">bootstrap aggregating</a>), <a href="/wiki/Boosting_(machine_learning)" title="Boosting (machine learning)">boosting</a> or stacking/blending techniques to induce high variance among the base models. Bagging creates diversity by generating random samples from the training observations and fitting the same model to each different sample — also known as <i>homogeneous parallel ensembles</i>. Boosting follows an iterative process by sequentially training each base model on the up-weighted errors of the previous base model, producing an additive model to reduce the final model errors — also known as <i>sequential ensemble learning</i>. Stacking or blending consists of different base models, each trained independently (i.e. diverse/high variance) to be combined into the ensemble model — producing a <i>heterogeneous parallel ensemble</i>. Common applications of ensemble learning include <a href="/wiki/Random_forest" title="Random forest">random forests</a> (an extension of bagging), Boosted Tree models, and <a href="/wiki/Gradient_boosting" title="Gradient boosting">Gradient Boosted</a> Tree Models. Models in applications of stacking are generally more task-specific — such as combining clustering techniques with other parametric and/or non-parametric techniques.<sup id="cite_ref-5" class="reference"><a href="#cite_note-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> </p><p>The broader term of <i>multiple classifier systems</i> also covers hybridization of hypotheses that are not induced by the same base learner.<sup class="noprint Inline-Template Template-Fact" style="white-space:nowrap;">[<i><a href="/wiki/Wikipedia:Citation_needed" title="Wikipedia:Citation needed"><span title="This claim needs references to reliable sources. (December 2017)">citation needed</span></a></i>]</sup> </p><p>Evaluating the prediction of an ensemble typically requires more computation than evaluating the prediction of a single model. In one sense, ensemble learning may be thought of as a way to compensate for poor learning algorithms by performing a lot of extra computation. On the other hand, the alternative is to do a lot more learning with one non-ensemble model. An ensemble may be more efficient at improving overall accuracy for the same increase in compute, storage, or communication resources by using that increase on two or more methods, than would have been improved by increasing resource use for a single method. Fast algorithms such as <a href="/wiki/Decision_tree_learning" title="Decision tree learning">decision trees</a> are commonly used in ensemble methods (e.g., random forests), although slower algorithms can benefit from ensemble techniques as well. </p><p>By analogy, ensemble techniques have been used also in <a href="/wiki/Unsupervised_learning" title="Unsupervised learning">unsupervised learning</a> scenarios, for example in <a href="/wiki/Consensus_clustering" title="Consensus clustering">consensus clustering</a> or in <a href="/wiki/Anomaly_detection" title="Anomaly detection">anomaly detection</a>. </p> <div class="mw-heading mw-heading2"><h2 id="Ensemble_theory">Ensemble theory</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=2" title="Edit section: Ensemble theory"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Empirically, ensembles tend to yield better results when there is a significant diversity among the models.<sup id="cite_ref-6" class="reference"><a href="#cite_note-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-7" class="reference"><a href="#cite_note-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup> Many ensemble methods, therefore, seek to promote diversity among the models they combine.<sup id="cite_ref-8" class="reference"><a href="#cite_note-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-9" class="reference"><a href="#cite_note-9"><span class="cite-bracket">[</span>9<span class="cite-bracket">]</span></a></sup> Although perhaps non-intuitive, more random algorithms (like random decision trees) can be used to produce a stronger ensemble than very deliberate algorithms (like entropy-reducing decision trees).<sup id="cite_ref-10" class="reference"><a href="#cite_note-10"><span class="cite-bracket">[</span>10<span class="cite-bracket">]</span></a></sup> Using a variety of strong learning algorithms, however, has been shown to be more effective than using techniques that attempt to <i>dumb-down</i> the models in order to promote diversity.<sup id="cite_ref-11" class="reference"><a href="#cite_note-11"><span class="cite-bracket">[</span>11<span class="cite-bracket">]</span></a></sup> It is possible to increase diversity in the training stage of the model using correlation for regression tasks <sup id="cite_ref-12" class="reference"><a href="#cite_note-12"><span class="cite-bracket">[</span>12<span class="cite-bracket">]</span></a></sup> or using information measures such as cross entropy for classification tasks.<sup id="cite_ref-13" class="reference"><a href="#cite_note-13"><span class="cite-bracket">[</span>13<span class="cite-bracket">]</span></a></sup> </p> <figure class="mw-halign-center" typeof="mw:File/Thumb"><a href="/wiki/File:Combining_multiple_classifiers.svg" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/2/29/Combining_multiple_classifiers.svg/400px-Combining_multiple_classifiers.svg.png" decoding="async" width="400" height="273" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/2/29/Combining_multiple_classifiers.svg/600px-Combining_multiple_classifiers.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/2/29/Combining_multiple_classifiers.svg/800px-Combining_multiple_classifiers.svg.png 2x" data-file-width="1080" data-file-height="736" /></a><figcaption>An ensemble of classifiers usually has smaller classification error than base models.</figcaption></figure> <p>Theoretically, one can justify the diversity concept because the lower bound of the error rate of an ensemble system can be decomposed into accuracy, diversity, and the other term.<sup id="cite_ref-14" class="reference"><a href="#cite_note-14"><span class="cite-bracket">[</span>14<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="The_geometric_framework">The geometric framework</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=3" title="Edit section: The geometric framework"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Ensemble learning, including both regression and classification tasks, can be explained using a geometric framework.<sup id="cite_ref-15" class="reference"><a href="#cite_note-15"><span class="cite-bracket">[</span>15<span class="cite-bracket">]</span></a></sup> Within this framework, the output of each individual classifier or regressor for the entire dataset can be viewed as a point in a multi-dimensional space. Additionally, the target result is also represented as a point in this space, referred to as the "ideal point." </p><p>The Euclidean distance is used as the metric to measure both the performance of a single classifier or regressor (the distance between its point and the ideal point) and the dissimilarity between two classifiers or regressors (the distance between their respective points). This perspective transforms ensemble learning into a deterministic problem. </p><p>For example, within this geometric framework, it can be proved that the averaging of the outputs (scores) of all base classifiers or regressors can lead to equal or better results than the average of all the individual models. It can also be proved that if the optimal weighting scheme is used, then a weighted averaging approach can outperform any of the individual classifiers or regressors that make up the ensemble or as good as the best performer at least. </p> <div class="mw-heading mw-heading2"><h2 id="Ensemble_size">Ensemble size</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=4" title="Edit section: Ensemble size"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>While the number of component classifiers of an ensemble has a great impact on the accuracy of prediction, there is a limited number of studies addressing this problem. <i>A priori</i> determining of ensemble size and the volume and velocity of big data streams make this even more crucial for online ensemble classifiers. Mostly statistical tests were used for determining the proper number of components. More recently, a theoretical framework suggested that there is an ideal number of component classifiers for an ensemble such that having more or less than this number of classifiers would deteriorate the accuracy. It is called "the law of diminishing returns in ensemble construction." Their theoretical framework shows that using the same number of independent component classifiers as class labels gives the highest accuracy.<sup id="cite_ref-16" class="reference"><a href="#cite_note-16"><span class="cite-bracket">[</span>16<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-17" class="reference"><a href="#cite_note-17"><span class="cite-bracket">[</span>17<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading2"><h2 id="Common_types_of_ensembles">Common types of ensembles</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=5" title="Edit section: Common types of ensembles"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <div class="mw-heading mw-heading3"><h3 id="Bayes_optimal_classifier">Bayes optimal classifier</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=6" title="Edit section: Bayes optimal classifier"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <style data-mw-deduplicate="TemplateStyles:r1236090951">.mw-parser-output .hatnote{font-style:italic}.mw-parser-output div.hatnote{padding-left:1.6em;margin-bottom:0.5em}.mw-parser-output .hatnote i{font-style:normal}.mw-parser-output .hatnote+link+.hatnote{margin-top:-0.5em}@media print{body.ns-0 .mw-parser-output .hatnote{display:none!important}}</style><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Bayes_classifier" title="Bayes classifier">Bayes classifier</a></div> <p>The Bayes optimal classifier is a classification technique. It is an ensemble of all the hypotheses in the hypothesis space. On average, no other ensemble can outperform it.<sup id="cite_ref-18" class="reference"><a href="#cite_note-18"><span class="cite-bracket">[</span>18<span class="cite-bracket">]</span></a></sup> The <a href="/wiki/Naive_Bayes_classifier" title="Naive Bayes classifier">Naive Bayes classifier</a> is a version of this that assumes that the data is conditionally independent on the class and makes the computation more feasible. Each hypothesis is given a vote proportional to the likelihood that the training dataset would be sampled from a system if that hypothesis were true. To facilitate training data of finite size, the vote of each hypothesis is also multiplied by the prior probability of that hypothesis. The Bayes optimal classifier can be expressed with the following equation: </p> <dl><dd><span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle y={\underset {c_{j}\in C}{\mathrm {argmax} }}\sum _{h_{i}\in H}{P(c_{j}|h_{i})P(T|h_{i})P(h_{i})}}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>y</mi> <mo>=</mo> <mrow class="MJX-TeXAtom-ORD"> <munder> <mrow class="MJX-TeXAtom-ORD"> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">g</mi> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">x</mi> </mrow> <mrow> <msub> <mi>c</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>j</mi> </mrow> </msub> <mo>∈</mo> <mi>C</mi> </mrow> </munder> </mrow> <munder> <mo>∑</mo> <mrow class="MJX-TeXAtom-ORD"> <msub> <mi>h</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>i</mi> </mrow> </msub> <mo>∈</mo> <mi>H</mi> </mrow> </munder> <mrow class="MJX-TeXAtom-ORD"> <mi>P</mi> <mo stretchy="false">(</mo> <msub> <mi>c</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>j</mi> </mrow> </msub> <mrow class="MJX-TeXAtom-ORD"> <mo stretchy="false">|</mo> </mrow> <msub> <mi>h</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>i</mi> </mrow> </msub> <mo stretchy="false">)</mo> <mi>P</mi> <mo stretchy="false">(</mo> <mi>T</mi> <mrow class="MJX-TeXAtom-ORD"> <mo stretchy="false">|</mo> </mrow> <msub> <mi>h</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>i</mi> </mrow> </msub> <mo stretchy="false">)</mo> <mi>P</mi> <mo stretchy="false">(</mo> <msub> <mi>h</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>i</mi> </mrow> </msub> <mo stretchy="false">)</mo> </mrow> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle y={\underset {c_{j}\in C}{\mathrm {argmax} }}\sum _{h_{i}\in H}{P(c_{j}|h_{i})P(T|h_{i})P(h_{i})}}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/09892e2a0091cfa48b8662fbf4c5f196689693b8" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -3.338ex; width:38.644ex; height:5.843ex;" alt="{\displaystyle y={\underset {c_{j}\in C}{\mathrm {argmax} }}\sum _{h_{i}\in H}{P(c_{j}|h_{i})P(T|h_{i})P(h_{i})}}"></span></dd></dl> <p>where <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle y}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>y</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle y}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/b8a6208ec717213d4317e666f1ae872e00620a0d" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:1.155ex; height:2.009ex;" alt="{\displaystyle y}"></span> is the predicted class, <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle C}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>C</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle C}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/4fc55753007cd3c18576f7933f6f089196732029" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.766ex; height:2.176ex;" alt="{\displaystyle C}"></span> is the set of all possible classes, <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle H}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>H</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle H}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/75a9edddcca2f782014371f75dca39d7e13a9c1b" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:2.064ex; height:2.176ex;" alt="{\displaystyle H}"></span> is the hypothesis space, <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle P}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>P</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle P}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/b4dc73bf40314945ff376bd363916a738548d40a" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.745ex; height:2.176ex;" alt="{\displaystyle P}"></span> refers to a <i>probability</i>, and <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle T}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>T</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle T}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/ec7200acd984a1d3a3d7dc455e262fbe54f7f6e0" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.636ex; height:2.176ex;" alt="{\displaystyle T}"></span> is the training data. As an ensemble, the Bayes optimal classifier represents a hypothesis that is not necessarily in <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle H}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>H</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle H}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/75a9edddcca2f782014371f75dca39d7e13a9c1b" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:2.064ex; height:2.176ex;" alt="{\displaystyle H}"></span>. The hypothesis represented by the Bayes optimal classifier, however, is the optimal hypothesis in <i>ensemble space</i> (the space of all possible ensembles consisting only of hypotheses in <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle H}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>H</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle H}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/75a9edddcca2f782014371f75dca39d7e13a9c1b" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:2.064ex; height:2.176ex;" alt="{\displaystyle H}"></span>). </p><p>This formula can be restated using <a href="/wiki/Bayes%27_theorem" title="Bayes' theorem">Bayes' theorem</a>, which says that the posterior is proportional to the likelihood times the prior: </p> <dl><dd><span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle P(h_{i}|T)\propto P(T|h_{i})P(h_{i})}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>P</mi> <mo stretchy="false">(</mo> <msub> <mi>h</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>i</mi> </mrow> </msub> <mrow class="MJX-TeXAtom-ORD"> <mo stretchy="false">|</mo> </mrow> <mi>T</mi> <mo stretchy="false">)</mo> <mo>∝</mo> <mi>P</mi> <mo stretchy="false">(</mo> <mi>T</mi> <mrow class="MJX-TeXAtom-ORD"> <mo stretchy="false">|</mo> </mrow> <msub> <mi>h</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>i</mi> </mrow> </msub> <mo stretchy="false">)</mo> <mi>P</mi> <mo stretchy="false">(</mo> <msub> <mi>h</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>i</mi> </mrow> </msub> <mo stretchy="false">)</mo> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle P(h_{i}|T)\propto P(T|h_{i})P(h_{i})}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/f2d5e48ddd526434d0f5f5c4acdf70ef5fd5042e" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:24.745ex; height:2.843ex;" alt="{\displaystyle P(h_{i}|T)\propto P(T|h_{i})P(h_{i})}"></span></dd></dl> <p>hence, </p> <dl><dd><span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle y={\underset {c_{j}\in C}{\mathrm {argmax} }}\sum _{h_{i}\in H}{P(c_{j}|h_{i})P(h_{i}|T)}}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>y</mi> <mo>=</mo> <mrow class="MJX-TeXAtom-ORD"> <munder> <mrow class="MJX-TeXAtom-ORD"> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">g</mi> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">x</mi> </mrow> <mrow> <msub> <mi>c</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>j</mi> </mrow> </msub> <mo>∈</mo> <mi>C</mi> </mrow> </munder> </mrow> <munder> <mo>∑</mo> <mrow class="MJX-TeXAtom-ORD"> <msub> <mi>h</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>i</mi> </mrow> </msub> <mo>∈</mo> <mi>H</mi> </mrow> </munder> <mrow class="MJX-TeXAtom-ORD"> <mi>P</mi> <mo stretchy="false">(</mo> <msub> <mi>c</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>j</mi> </mrow> </msub> <mrow class="MJX-TeXAtom-ORD"> <mo stretchy="false">|</mo> </mrow> <msub> <mi>h</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>i</mi> </mrow> </msub> <mo stretchy="false">)</mo> <mi>P</mi> <mo stretchy="false">(</mo> <msub> <mi>h</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>i</mi> </mrow> </msub> <mrow class="MJX-TeXAtom-ORD"> <mo stretchy="false">|</mo> </mrow> <mi>T</mi> <mo stretchy="false">)</mo> </mrow> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle y={\underset {c_{j}\in C}{\mathrm {argmax} }}\sum _{h_{i}\in H}{P(c_{j}|h_{i})P(h_{i}|T)}}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/48d556eedab703a192d805d526ab36c334081c58" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -3.338ex; width:32.951ex; height:5.843ex;" alt="{\displaystyle y={\underset {c_{j}\in C}{\mathrm {argmax} }}\sum _{h_{i}\in H}{P(c_{j}|h_{i})P(h_{i}|T)}}"></span></dd></dl> <div class="mw-heading mw-heading3"><h3 id="Bootstrap_aggregating_(bagging)"><span id="Bootstrap_aggregating_.28bagging.29"></span>Bootstrap aggregating (bagging)</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=7" title="Edit section: Bootstrap aggregating (bagging)"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951"><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Bootstrap_aggregating" title="Bootstrap aggregating">Bootstrap aggregating</a></div><figure class="mw-default-size" typeof="mw:File/Thumb"><a href="/wiki/File:Bootstrap_set_generation.png" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/1/1e/Bootstrap_set_generation.png/170px-Bootstrap_set_generation.png" decoding="async" width="170" height="221" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/1/1e/Bootstrap_set_generation.png 1.5x" data-file-width="224" data-file-height="291" /></a><figcaption>Three datasets bootstrapped from an original set. Example A occurs twice in set 1 because these are chosen with replacement.</figcaption></figure><p>Bootstrap aggregation (<i>bagging</i>) involves training an ensemble on <i>bootstrapped</i> data sets. A bootstrapped set is created by selecting from original training data set with replacement. Thus, a bootstrap set may contain a given example zero, one, or multiple times. Ensemble members can also have limits on the features (e.g., nodes of a decision tree), to encourage exploring of diverse features.<sup id="cite_ref-19" class="reference"><a href="#cite_note-19"><span class="cite-bracket">[</span>19<span class="cite-bracket">]</span></a></sup> The variance of local information in the bootstrap sets and feature considerations promote diversity in the ensemble, and can strengthen the ensemble.<sup id="cite_ref-20" class="reference"><a href="#cite_note-20"><span class="cite-bracket">[</span>20<span class="cite-bracket">]</span></a></sup> To reduce overfitting, a member can be validated using the out-of-bag set (the examples that are not in its bootstrap set).<sup id="cite_ref-21" class="reference"><a href="#cite_note-21"><span class="cite-bracket">[</span>21<span class="cite-bracket">]</span></a></sup> </p><p>Inference is done by <b>voting</b> of predictions of ensemble members, called <b>aggregation</b>. It is illustrated below with an ensemble of four decision trees. The query example is classified by each tree. Because three of the four predict the <i>positive</i> class, the ensemble's overall classification is <i>positive</i>. <a href="/wiki/Random_forest" title="Random forest">Random forests</a> like the one shown are a common application of bagging. </p> <figure class="mw-halign-center" typeof="mw:File/Frameless"><a href="/wiki/File:Ensemble_Aggregation.png" class="mw-file-description" title="An example of the aggregation process for an ensemble of decision trees. Individual classifications are aggregated, and an overall classification is derived."><img alt="An example of the aggregation process for an ensemble of decision trees. Individual classifications are aggregated, and an overall classification is derived." src="//upload.wikimedia.org/wikipedia/commons/thumb/4/4a/Ensemble_Aggregation.png/615px-Ensemble_Aggregation.png" decoding="async" width="615" height="283" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/4/4a/Ensemble_Aggregation.png/923px-Ensemble_Aggregation.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/4/4a/Ensemble_Aggregation.png/1230px-Ensemble_Aggregation.png 2x" data-file-width="1296" data-file-height="596" /></a><figcaption>An example of the aggregation process for an ensemble of decision trees. Individual classifications are aggregated, and an overall classification is derived.</figcaption></figure> <div class="mw-heading mw-heading3"><h3 id="Boosting">Boosting</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=8" title="Edit section: Boosting"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951"><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Boosting_(meta-algorithm)" class="mw-redirect" title="Boosting (meta-algorithm)">Boosting (meta-algorithm)</a></div> <p>Boosting involves training successive models by emphasizing training data mis-classified by previously learned models. Initially, all data (D1) has equal weight and is used to learn a base model M1. The examples mis-classified by M1 are assigned a weight greater than correctly classified examples. This boosted data (D2) is used to train a second base model M2, and so on. Inference is done by voting. </p><p>In some cases, boosting has yielded better accuracy than bagging, but tends to over-fit more. The most common implementation of boosting is <a href="/wiki/Adaboost" class="mw-redirect" title="Adaboost">Adaboost</a>, but some newer algorithms are reported to achieve better results.<sup class="noprint Inline-Template Template-Fact" style="white-space:nowrap;">[<i><a href="/wiki/Wikipedia:Citation_needed" title="Wikipedia:Citation needed"><span title="This claim needs references to reliable sources. (January 2012)">citation needed</span></a></i>]</sup> </p> <div class="mw-heading mw-heading3"><h3 id="Bayesian_model_averaging">Bayesian model averaging</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=9" title="Edit section: Bayesian model averaging"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Bayesian model averaging (BMA) makes predictions by averaging the predictions of models weighted by their posterior probabilities given the data.<sup id="cite_ref-22" class="reference"><a href="#cite_note-22"><span class="cite-bracket">[</span>22<span class="cite-bracket">]</span></a></sup> BMA is known to generally give better answers than a single model, obtained, e.g., via <a href="/wiki/Stepwise_regression" title="Stepwise regression">stepwise regression</a>, especially where very different models have nearly identical performance in the training set but may otherwise perform quite differently. </p><p>The question with any use of <a href="/wiki/Bayes%27_theorem" title="Bayes' theorem">Bayes' theorem</a> is the prior, i.e., the probability (perhaps subjective) that each model is the best to use for a given purpose. Conceptually, BMA can be used with any prior. <i>R</i> packages ensembleBMA<sup id="cite_ref-23" class="reference"><a href="#cite_note-23"><span class="cite-bracket">[</span>23<span class="cite-bracket">]</span></a></sup> and BMA<sup id="cite_ref-24" class="reference"><a href="#cite_note-24"><span class="cite-bracket">[</span>24<span class="cite-bracket">]</span></a></sup> use the prior implied by the <a href="/wiki/Bayesian_information_criterion" title="Bayesian information criterion">Bayesian information criterion</a>, (BIC), following Raftery (1995).<sup id="cite_ref-25" class="reference"><a href="#cite_note-25"><span class="cite-bracket">[</span>25<span class="cite-bracket">]</span></a></sup> <i>R</i> package BAS supports the use of the priors implied by <a href="/wiki/Akaike_information_criterion" title="Akaike information criterion">Akaike information criterion</a> (AIC) and other criteria over the alternative models as well as priors over the coefficients.<sup id="cite_ref-26" class="reference"><a href="#cite_note-26"><span class="cite-bracket">[</span>26<span class="cite-bracket">]</span></a></sup> </p><p>The difference between BIC and AIC is the strength of preference for parsimony. BIC's penalty for model complexity is <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \ln(n)k}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>ln</mi> <mo>⁡</mo> <mo stretchy="false">(</mo> <mi>n</mi> <mo stretchy="false">)</mo> <mi>k</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \ln(n)k}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/8b5e1fe482e32832d5e1181a90e467a125d4af9e" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:6.355ex; height:2.843ex;" alt="{\displaystyle \ln(n)k}"></span> , while AIC's is <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle 2k}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mn>2</mn> <mi>k</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle 2k}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/ab358eb7defb4d2b0fc1f9e8a4e2d189fe600eb6" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:2.374ex; height:2.176ex;" alt="{\displaystyle 2k}"></span>. Large-sample asymptotic theory establishes that if there is a best model, then with increasing sample sizes, BIC is strongly consistent, i.e., will almost certainly find it, while AIC may not, because AIC may continue to place excessive posterior probability on models that are more complicated than they need to be. On the other hand, AIC and AICc are asymptotically "efficient" (i.e., minimum mean square prediction error), while BIC is not .<sup id="cite_ref-27" class="reference"><a href="#cite_note-27"><span class="cite-bracket">[</span>27<span class="cite-bracket">]</span></a></sup> </p><p>Haussler et al. (1994) showed that when BMA is used for classification, its expected error is at most twice the expected error of the Bayes optimal classifier.<sup id="cite_ref-28" class="reference"><a href="#cite_note-28"><span class="cite-bracket">[</span>28<span class="cite-bracket">]</span></a></sup> Burnham and Anderson (1998, 2002) contributed greatly to introducing a wider audience to the basic ideas of Bayesian model averaging and popularizing the methodology.<sup id="cite_ref-29" class="reference"><a href="#cite_note-29"><span class="cite-bracket">[</span>29<span class="cite-bracket">]</span></a></sup> The availability of software, including other free open-source packages for <a href="/wiki/R_(programming_language)" title="R (programming language)">R</a> beyond those mentioned above, helped make the methods accessible to a wider audience.<sup id="cite_ref-30" class="reference"><a href="#cite_note-30"><span class="cite-bracket">[</span>30<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Bayesian_model_combination">Bayesian model combination</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=10" title="Edit section: Bayesian model combination"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Bayesian model combination (BMC) is an algorithmic correction to Bayesian model averaging (BMA). Instead of sampling each model in the ensemble individually, it samples from the space of possible ensembles (with model weights drawn randomly from a Dirichlet distribution having uniform parameters). This modification overcomes the tendency of BMA to converge toward giving all the weight to a single model. Although BMC is somewhat more computationally expensive than BMA, it tends to yield dramatically better results. BMC has been shown to be better on average (with statistical significance) than BMA and bagging.<sup id="cite_ref-31" class="reference"><a href="#cite_note-31"><span class="cite-bracket">[</span>31<span class="cite-bracket">]</span></a></sup> </p><p>Use of Bayes' law to compute model weights requires computing the probability of the data given each model. Typically, none of the models in the ensemble are exactly the distribution from which the training data were generated, so all of them correctly receive a value close to zero for this term. This would work well if the ensemble were big enough to sample the entire model-space, but this is rarely possible. Consequently, each pattern in the training data will cause the ensemble weight to shift toward the model in the ensemble that is closest to the distribution of the training data. It essentially reduces to an unnecessarily complex method for doing model selection. </p><p>The possible weightings for an ensemble can be visualized as lying on a simplex. At each vertex of the simplex, all of the weight is given to a single model in the ensemble. BMA converges toward the vertex that is closest to the distribution of the training data. By contrast, BMC converges toward the point where this distribution projects onto the simplex. In other words, instead of selecting the one model that is closest to the generating distribution, it seeks the combination of models that is closest to the generating distribution. </p><p>The results from BMA can often be approximated by using cross-validation to select the best model from a bucket of models. Likewise, the results from BMC may be approximated by using cross-validation to select the best ensemble combination from a random sampling of possible weightings. </p> <div class="mw-heading mw-heading3"><h3 id="Bucket_of_models">Bucket of models</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=11" title="Edit section: Bucket of models"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>A "bucket of models" is an ensemble technique in which a model selection algorithm is used to choose the best model for each problem. When tested with only one problem, a bucket of models can produce no better results than the best model in the set, but when evaluated across many problems, it will typically produce much better results, on average, than any model in the set. </p><p>The most common approach used for model-selection is <a href="/wiki/Cross-validation_(statistics)" title="Cross-validation (statistics)">cross-validation</a> selection (sometimes called a "bake-off contest"). It is described with the following pseudo-code: </p> <pre>For each model m in the bucket: Do c times: (where 'c' is some constant) Randomly divide the training dataset into two sets: A and B Train m with A Test m with B Select the model that obtains the highest average score </pre> <p>Cross-Validation Selection can be summed up as: "try them all with the training set, and pick the one that works best".<sup id="cite_ref-32" class="reference"><a href="#cite_note-32"><span class="cite-bracket">[</span>32<span class="cite-bracket">]</span></a></sup> </p><p>Gating is a generalization of Cross-Validation Selection. It involves training another learning model to decide which of the models in the bucket is best-suited to solve the problem. Often, a <a href="/wiki/Perceptron" title="Perceptron">perceptron</a> is used for the gating model. It can be used to pick the "best" model, or it can be used to give a linear weight to the predictions from each model in the bucket. </p><p>When a bucket of models is used with a large set of problems, it may be desirable to avoid training some of the models that take a long time to train. Landmark learning is a meta-learning approach that seeks to solve this problem. It involves training only the fast (but imprecise) algorithms in the bucket, and then using the performance of these algorithms to help determine which slow (but accurate) algorithm is most likely to do best.<sup id="cite_ref-33" class="reference"><a href="#cite_note-33"><span class="cite-bracket">[</span>33<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Amended_Cross-Entropy_Cost:_An_Approach_for_Encouraging_Diversity_in_Classification_Ensemble">Amended Cross-Entropy Cost: An Approach for Encouraging Diversity in Classification Ensemble</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=12" title="Edit section: Amended Cross-Entropy Cost: An Approach for Encouraging Diversity in Classification Ensemble"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>The most common approach for training classifier is using <a href="/wiki/Cross-entropy" title="Cross-entropy">Cross-entropy</a> cost function. However, one would like to train an ensemble of models that have diversity so when we combine them it would provide best results.<sup id="cite_ref-34" class="reference"><a href="#cite_note-34"><span class="cite-bracket">[</span>34<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-35" class="reference"><a href="#cite_note-35"><span class="cite-bracket">[</span>35<span class="cite-bracket">]</span></a></sup> Assuming we use a simple ensemble of averaging <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle K}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>K</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle K}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/2b76fce82a62ed5461908f0dc8f037de4e3686b0" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:2.066ex; height:2.176ex;" alt="{\displaystyle K}"></span> classifiers. Then the Amended Cross-Entropy Cost is </p> <dl><dd><span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle e^{k}=H(p,q^{k})-{\frac {\lambda }{K}}\sum _{j\neq k}H(q^{j},q^{k})}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <msup> <mi>e</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>k</mi> </mrow> </msup> <mo>=</mo> <mi>H</mi> <mo stretchy="false">(</mo> <mi>p</mi> <mo>,</mo> <msup> <mi>q</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>k</mi> </mrow> </msup> <mo stretchy="false">)</mo> <mo>−</mo> <mrow class="MJX-TeXAtom-ORD"> <mfrac> <mi>λ</mi> <mi>K</mi> </mfrac> </mrow> <munder> <mo>∑</mo> <mrow class="MJX-TeXAtom-ORD"> <mi>j</mi> <mo>≠</mo> <mi>k</mi> </mrow> </munder> <mi>H</mi> <mo stretchy="false">(</mo> <msup> <mi>q</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>j</mi> </mrow> </msup> <mo>,</mo> <msup> <mi>q</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>k</mi> </mrow> </msup> <mo stretchy="false">)</mo> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle e^{k}=H(p,q^{k})-{\frac {\lambda }{K}}\sum _{j\neq k}H(q^{j},q^{k})}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/7b934b00175e50e1b9a6374682c24bb2ea034761" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -3.505ex; width:32.451ex; height:7.009ex;" alt="{\displaystyle e^{k}=H(p,q^{k})-{\frac {\lambda }{K}}\sum _{j\neq k}H(q^{j},q^{k})}"></span></dd></dl> <p>where <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle e^{k}}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <msup> <mi>e</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>k</mi> </mrow> </msup> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle e^{k}}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/5eedc905f8f6e254ae23c7d7e10ca32731449ad0" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:2.172ex; height:2.676ex;" alt="{\displaystyle e^{k}}"></span> is the cost function of the <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle k^{th}}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <msup> <mi>k</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>t</mi> <mi>h</mi> </mrow> </msup> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle k^{th}}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/c348e1c6f8200f15d1d6026fc140d554b272096d" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:2.984ex; height:2.676ex;" alt="{\displaystyle k^{th}}"></span> classifier, <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle q^{k}}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <msup> <mi>q</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>k</mi> </mrow> </msup> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle q^{k}}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/fc9b1525f5a653e19f9fd37fd2701a768e171632" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:2.168ex; height:3.009ex;" alt="{\displaystyle q^{k}}"></span> is the probability of the <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle k^{th}}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <msup> <mi>k</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>t</mi> <mi>h</mi> </mrow> </msup> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle k^{th}}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/c348e1c6f8200f15d1d6026fc140d554b272096d" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:2.984ex; height:2.676ex;" alt="{\displaystyle k^{th}}"></span> classifier, <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle p}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>p</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle p}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/81eac1e205430d1f40810df36a0edffdc367af36" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; margin-left: -0.089ex; width:1.259ex; height:2.009ex;" alt="{\displaystyle p}"></span> is the true probability that we need to estimate and <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \lambda }"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>λ</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \lambda }</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/b43d0ea3c9c025af1be9128e62a18fa74bedda2a" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.355ex; height:2.176ex;" alt="{\displaystyle \lambda }"></span> is a parameter between 0 and 1 that define the diversity that we would like to establish. When <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \lambda =0}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>λ</mi> <mo>=</mo> <mn>0</mn> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \lambda =0}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/00c4bba30544017fe76932de5a4e25adb5512d95" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:5.616ex; height:2.176ex;" alt="{\displaystyle \lambda =0}"></span> we want each classifier to do its best regardless of the ensemble and when <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \lambda =1}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>λ</mi> <mo>=</mo> <mn>1</mn> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \lambda =1}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/543b4490416437b7c80ea473bbcac0e4ab7a7f11" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:5.616ex; height:2.176ex;" alt="{\displaystyle \lambda =1}"></span> we would like the classifier to be as diverse as possible. </p> <div class="mw-heading mw-heading3"><h3 id="Stacking">Stacking</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=13" title="Edit section: Stacking"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Stacking (sometimes called <i>stacked generalization</i>) involves training a model to combine the predictions of several other learning algorithms. First, all of the other algorithms are trained using the available data, then a combiner algorithm (final estimator) is trained to make a final prediction using all the predictions of the other algorithms (base estimators) as additional inputs or using cross-validated predictions from the base estimators which can prevent overfitting.<sup id="cite_ref-36" class="reference"><a href="#cite_note-36"><span class="cite-bracket">[</span>36<span class="cite-bracket">]</span></a></sup> If an arbitrary combiner algorithm is used, then stacking can theoretically represent any of the ensemble techniques described in this article, although, in practice, a <a href="/wiki/Logistic_regression" title="Logistic regression">logistic regression</a> model is often used as the combiner. </p><p>Stacking typically yields performance better than any single one of the trained models.<sup id="cite_ref-37" class="reference"><a href="#cite_note-37"><span class="cite-bracket">[</span>37<span class="cite-bracket">]</span></a></sup> It has been successfully used on both supervised learning tasks (regression,<sup id="cite_ref-38" class="reference"><a href="#cite_note-38"><span class="cite-bracket">[</span>38<span class="cite-bracket">]</span></a></sup> classification and distance learning <sup id="cite_ref-39" class="reference"><a href="#cite_note-39"><span class="cite-bracket">[</span>39<span class="cite-bracket">]</span></a></sup>) and unsupervised learning (density estimation).<sup id="cite_ref-40" class="reference"><a href="#cite_note-40"><span class="cite-bracket">[</span>40<span class="cite-bracket">]</span></a></sup> It has also been used to estimate bagging's error rate.<sup id="cite_ref-Rokach2010_3-1" class="reference"><a href="#cite_note-Rokach2010-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-41" class="reference"><a href="#cite_note-41"><span class="cite-bracket">[</span>41<span class="cite-bracket">]</span></a></sup> It has been reported to out-perform Bayesian model-averaging.<sup id="cite_ref-42" class="reference"><a href="#cite_note-42"><span class="cite-bracket">[</span>42<span class="cite-bracket">]</span></a></sup> The two top-performers in the Netflix competition utilized blending, which may be considered a form of stacking.<sup id="cite_ref-43" class="reference"><a href="#cite_note-43"><span class="cite-bracket">[</span>43<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Voting">Voting</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=14" title="Edit section: Voting"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Voting is another form of ensembling. See e.g. <a href="/wiki/Weighted_majority_algorithm_(machine_learning)" title="Weighted majority algorithm (machine learning)">Weighted majority algorithm (machine learning)</a>. </p> <div class="mw-heading mw-heading2"><h2 id="Implementations_in_statistics_packages">Implementations in statistics packages</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=15" title="Edit section: Implementations in statistics packages"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li><a href="/wiki/R_(programming_language)" title="R (programming language)">R</a>: at least three packages offer Bayesian model averaging tools,<sup id="cite_ref-44" class="reference"><a href="#cite_note-44"><span class="cite-bracket">[</span>44<span class="cite-bracket">]</span></a></sup> including the <style data-mw-deduplicate="TemplateStyles:r886049734">.mw-parser-output .monospaced{font-family:monospace,monospace}</style><span class="monospaced">BMS</span> (an acronym for Bayesian Model Selection) package,<sup id="cite_ref-45" class="reference"><a href="#cite_note-45"><span class="cite-bracket">[</span>45<span class="cite-bracket">]</span></a></sup> the <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r886049734"><span class="monospaced">BAS</span> (an acronym for Bayesian Adaptive Sampling) package,<sup id="cite_ref-46" class="reference"><a href="#cite_note-46"><span class="cite-bracket">[</span>46<span class="cite-bracket">]</span></a></sup> and the <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r886049734"><span class="monospaced">BMA</span> package.<sup id="cite_ref-47" class="reference"><a href="#cite_note-47"><span class="cite-bracket">[</span>47<span class="cite-bracket">]</span></a></sup></li> <li><a href="/wiki/Python_(programming_language)" title="Python (programming language)">Python</a>: <a href="/wiki/Scikit-learn" title="Scikit-learn">scikit-learn</a>, a package for machine learning in Python offers packages for ensemble learning including packages for bagging, voting and averaging methods.</li> <li><a href="/wiki/MATLAB" title="MATLAB">MATLAB</a>: classification ensembles are implemented in Statistics and Machine Learning Toolbox.<sup id="cite_ref-48" class="reference"><a href="#cite_note-48"><span class="cite-bracket">[</span>48<span class="cite-bracket">]</span></a></sup></li></ul> <div class="mw-heading mw-heading2"><h2 id="Ensemble_learning_applications">Ensemble learning applications</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=16" title="Edit section: Ensemble learning applications"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>In recent years, due to growing computational power, which allows for training in large ensemble learning in a reasonable time frame, the number of ensemble learning applications has grown increasingly.<sup id="cite_ref-s1_49-0" class="reference"><a href="#cite_note-s1-49"><span class="cite-bracket">[</span>49<span class="cite-bracket">]</span></a></sup> Some of the applications of ensemble classifiers include: </p> <div class="mw-heading mw-heading3"><h3 id="Remote_sensing">Remote sensing</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=17" title="Edit section: Remote sensing"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951"><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Remote_sensing" title="Remote sensing">Remote sensing</a></div> <div class="mw-heading mw-heading4"><h4 id="Land_cover_mapping">Land cover mapping</h4><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=18" title="Edit section: Land cover mapping"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p><a href="/wiki/Image_analysis#Land_cover_mapping" title="Image analysis">Land cover mapping</a> is one of the major applications of <a href="/wiki/Earth_observation_satellite" title="Earth observation satellite">Earth observation satellite</a> sensors, using <a href="/wiki/Remote_sensing" title="Remote sensing">remote sensing</a> and <a href="/wiki/Geospatial_data" class="mw-redirect" title="Geospatial data">geospatial data</a>, to identify the materials and objects which are located on the surface of target areas. Generally, the classes of target materials include roads, buildings, rivers, lakes, and vegetation.<sup id="cite_ref-rodriguez_50-0" class="reference"><a href="#cite_note-rodriguez-50"><span class="cite-bracket">[</span>50<span class="cite-bracket">]</span></a></sup> Some different ensemble learning approaches based on <a href="/wiki/Artificial_neural_networks" class="mw-redirect" title="Artificial neural networks">artificial neural networks</a>,<sup id="cite_ref-51" class="reference"><a href="#cite_note-51"><span class="cite-bracket">[</span>51<span class="cite-bracket">]</span></a></sup> <a href="/wiki/Kernel_principal_component_analysis" title="Kernel principal component analysis">kernel principal component analysis</a> (KPCA),<sup id="cite_ref-52" class="reference"><a href="#cite_note-52"><span class="cite-bracket">[</span>52<span class="cite-bracket">]</span></a></sup> <a href="/wiki/Decision_trees" class="mw-redirect" title="Decision trees">decision trees</a> with <a href="/wiki/Boosting_(machine_learning)" title="Boosting (machine learning)">boosting</a>,<sup id="cite_ref-53" class="reference"><a href="#cite_note-53"><span class="cite-bracket">[</span>53<span class="cite-bracket">]</span></a></sup> <a href="/wiki/Random_forest" title="Random forest">random forest</a><sup id="cite_ref-rodriguez_50-1" class="reference"><a href="#cite_note-rodriguez-50"><span class="cite-bracket">[</span>50<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-54" class="reference"><a href="#cite_note-54"><span class="cite-bracket">[</span>54<span class="cite-bracket">]</span></a></sup> and automatic design of multiple classifier systems,<sup id="cite_ref-55" class="reference"><a href="#cite_note-55"><span class="cite-bracket">[</span>55<span class="cite-bracket">]</span></a></sup> are proposed to efficiently identify <a href="/wiki/Land_cover" title="Land cover">land cover</a> objects. </p> <div class="mw-heading mw-heading4"><h4 id="Change_detection">Change detection</h4><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=19" title="Edit section: Change detection"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p><a href="/wiki/Change_detection_(GIS)" title="Change detection (GIS)">Change detection</a> is an <a href="/wiki/Image_analysis" title="Image analysis">image analysis</a> problem, consisting of the identification of places where the <a href="/wiki/Land_cover" title="Land cover">land cover</a> has changed over time. <a href="/wiki/Change_detection_(GIS)" title="Change detection (GIS)">Change detection</a> is widely used in fields such as <a href="/wiki/Urban_growth" class="mw-redirect" title="Urban growth">urban growth</a>, <a href="/wiki/Forest_dynamics" title="Forest dynamics">forest and vegetation dynamics</a>, <a href="/wiki/Land_use" title="Land use">land use</a> and <a href="/wiki/Disaster_Monitoring_Constellation" title="Disaster Monitoring Constellation">disaster monitoring</a>.<sup id="cite_ref-s2_56-0" class="reference"><a href="#cite_note-s2-56"><span class="cite-bracket">[</span>56<span class="cite-bracket">]</span></a></sup> The earliest applications of ensemble classifiers in change detection are designed with the majority <a href="/wiki/Plurality_voting" title="Plurality voting">voting</a>,<sup id="cite_ref-57" class="reference"><a href="#cite_note-57"><span class="cite-bracket">[</span>57<span class="cite-bracket">]</span></a></sup> <a href="/wiki/Bayesian_model_averaging" class="mw-redirect" title="Bayesian model averaging">Bayesian model averaging</a>,<sup id="cite_ref-58" class="reference"><a href="#cite_note-58"><span class="cite-bracket">[</span>58<span class="cite-bracket">]</span></a></sup> and the <a href="/wiki/Maximum_posterior_probability" class="mw-redirect" title="Maximum posterior probability">maximum posterior probability</a>.<sup id="cite_ref-Bruzzone_et_al_2002_59-0" class="reference"><a href="#cite_note-Bruzzone_et_al_2002-59"><span class="cite-bracket">[</span>59<span class="cite-bracket">]</span></a></sup> Given the growth of satellite data over time, the past decade sees more use of time series methods for continuous change detection from image stacks.<sup id="cite_ref-60" class="reference"><a href="#cite_note-60"><span class="cite-bracket">[</span>60<span class="cite-bracket">]</span></a></sup> One example is a Bayesian ensemble changepoint detection method called BEAST, with the software available as a package Rbeast in R, Python, and Matlab.<sup id="cite_ref-61" class="reference"><a href="#cite_note-61"><span class="cite-bracket">[</span>61<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Computer_security">Computer security</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=20" title="Edit section: Computer security"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <div class="mw-heading mw-heading4"><h4 id="Distributed_denial_of_service">Distributed denial of service</h4><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=21" title="Edit section: Distributed denial of service"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p><a href="/wiki/Denial-of-service_attack" title="Denial-of-service attack">Distributed denial of service</a> is one of the most threatening <a href="/wiki/Cyber-attack" class="mw-redirect" title="Cyber-attack">cyber-attacks</a> that may happen to an <a href="/wiki/Internet_service_provider" title="Internet service provider">internet service provider</a>.<sup id="cite_ref-s1_49-1" class="reference"><a href="#cite_note-s1-49"><span class="cite-bracket">[</span>49<span class="cite-bracket">]</span></a></sup> By combining the output of single classifiers, ensemble classifiers reduce the total error of detecting and discriminating such attacks from legitimate <a href="/wiki/Flash_crowd" class="mw-redirect" title="Flash crowd">flash crowds</a>.<sup id="cite_ref-62" class="reference"><a href="#cite_note-62"><span class="cite-bracket">[</span>62<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading4"><h4 id="Malware_Detection">Malware Detection</h4><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=22" title="Edit section: Malware Detection"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Classification of <a href="/wiki/Malware" title="Malware">malware</a> codes such as <a href="/wiki/Computer_virus" title="Computer virus">computer viruses</a>, <a href="/wiki/Computer_worm" title="Computer worm">computer worms</a>, <a href="/wiki/Trojan_horses" class="mw-redirect" title="Trojan horses">trojans</a>, <a href="/wiki/Ransomware" title="Ransomware">ransomware</a> and <a href="/wiki/Spyware" title="Spyware">spywares</a> with the usage of <a href="/wiki/Machine_learning" title="Machine learning">machine learning</a> techniques, is inspired by the <a href="/wiki/Document_classification" title="Document classification">document categorization problem</a>.<sup id="cite_ref-63" class="reference"><a href="#cite_note-63"><span class="cite-bracket">[</span>63<span class="cite-bracket">]</span></a></sup> Ensemble learning systems have shown a proper efficacy in this area.<sup id="cite_ref-64" class="reference"><a href="#cite_note-64"><span class="cite-bracket">[</span>64<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-65" class="reference"><a href="#cite_note-65"><span class="cite-bracket">[</span>65<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading4"><h4 id="Intrusion_detection">Intrusion detection</h4><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=23" title="Edit section: Intrusion detection"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>An <a href="/wiki/Intrusion_detection_system" title="Intrusion detection system">intrusion detection system</a> monitors <a href="/wiki/Computer_network" title="Computer network">computer network</a> or <a href="/wiki/Computer_system" class="mw-redirect" title="Computer system">computer systems</a> to identify intruder codes like an <a href="/wiki/Anomaly_detection" title="Anomaly detection">anomaly detection</a> process. Ensemble learning successfully aids such monitoring systems to reduce their total error.<sup id="cite_ref-66" class="reference"><a href="#cite_note-66"><span class="cite-bracket">[</span>66<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-67" class="reference"><a href="#cite_note-67"><span class="cite-bracket">[</span>67<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Face_recognition">Face recognition</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=24" title="Edit section: Face recognition"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951"><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Face_recognition" class="mw-redirect" title="Face recognition">Face recognition</a></div> <p><a href="/wiki/Face_recognition" class="mw-redirect" title="Face recognition">Face recognition</a>, which recently has become one of the most popular research areas of <a href="/wiki/Pattern_recognition" title="Pattern recognition">pattern recognition</a>, copes with identification or verification of a person by their <a href="/wiki/Digital_image" title="Digital image">digital images</a>.<sup id="cite_ref-68" class="reference"><a href="#cite_note-68"><span class="cite-bracket">[</span>68<span class="cite-bracket">]</span></a></sup> </p><p>Hierarchical ensembles based on Gabor Fisher classifier and <a href="/wiki/Independent_component_analysis" title="Independent component analysis">independent component analysis</a> <a href="/wiki/Data_pre-processing" class="mw-redirect" title="Data pre-processing">preprocessing</a> techniques are some of the earliest ensembles employed in this field.<sup id="cite_ref-69" class="reference"><a href="#cite_note-69"><span class="cite-bracket">[</span>69<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-70" class="reference"><a href="#cite_note-70"><span class="cite-bracket">[</span>70<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-71" class="reference"><a href="#cite_note-71"><span class="cite-bracket">[</span>71<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Emotion_recognition">Emotion recognition</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=25" title="Edit section: Emotion recognition"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951"><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Emotion_recognition" title="Emotion recognition">Emotion recognition</a></div> <p>While <a href="/wiki/Speech_recognition" title="Speech recognition">speech recognition</a> is mainly based on <a href="/wiki/Deep_learning" title="Deep learning">deep learning</a> because most of the industry players in this field like <a href="/wiki/Google" title="Google">Google</a>, <a href="/wiki/Microsoft" title="Microsoft">Microsoft</a> and <a href="/wiki/IBM" title="IBM">IBM</a> reveal that the core technology of their <a href="/wiki/Speech_recognition" title="Speech recognition">speech recognition</a> is based on this approach, speech-based <a href="/wiki/Emotion_recognition" title="Emotion recognition">emotion recognition</a> can also have a satisfactory performance with ensemble learning.<sup id="cite_ref-72" class="reference"><a href="#cite_note-72"><span class="cite-bracket">[</span>72<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-73" class="reference"><a href="#cite_note-73"><span class="cite-bracket">[</span>73<span class="cite-bracket">]</span></a></sup> </p><p>It is also being successfully used in <a href="/wiki/Emotion_recognition" title="Emotion recognition">facial emotion recognition</a>.<sup id="cite_ref-74" class="reference"><a href="#cite_note-74"><span class="cite-bracket">[</span>74<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-75" class="reference"><a href="#cite_note-75"><span class="cite-bracket">[</span>75<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-76" class="reference"><a href="#cite_note-76"><span class="cite-bracket">[</span>76<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Fraud_detection">Fraud detection</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=26" title="Edit section: Fraud detection"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951"><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Fraud_detection" class="mw-redirect" title="Fraud detection">Fraud detection</a></div> <p><a href="/wiki/Fraud_detection" class="mw-redirect" title="Fraud detection">Fraud detection</a> deals with the identification of <a href="/wiki/Bank_fraud" title="Bank fraud">bank fraud</a>, such as <a href="/wiki/Money_laundering" title="Money laundering">money laundering</a>, <a href="/wiki/Credit_card_fraud" title="Credit card fraud">credit card fraud</a> and <a href="/w/index.php?title=Telecommunication_fraud&action=edit&redlink=1" class="new" title="Telecommunication fraud (page does not exist)">telecommunication fraud</a>, which have vast domains of research and applications of <a href="/wiki/Machine_learning" title="Machine learning">machine learning</a>. Because ensemble learning improves the robustness of the normal behavior modelling, it has been proposed as an efficient technique to detect such fraudulent cases and activities in banking and credit card systems.<sup id="cite_ref-77" class="reference"><a href="#cite_note-77"><span class="cite-bracket">[</span>77<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-78" class="reference"><a href="#cite_note-78"><span class="cite-bracket">[</span>78<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Financial_decision-making">Financial decision-making</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=27" title="Edit section: Financial decision-making"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>The accuracy of prediction of business failure is a very crucial issue in financial decision-making. Therefore, different ensemble classifiers are proposed to predict <a href="/wiki/Financial_crisis" title="Financial crisis">financial crises</a> and <a href="/wiki/Financial_distress" title="Financial distress">financial distress</a>.<sup id="cite_ref-ReferenceA_79-0" class="reference"><a href="#cite_note-ReferenceA-79"><span class="cite-bracket">[</span>79<span class="cite-bracket">]</span></a></sup> Also, in the <a href="/wiki/Market_manipulation" title="Market manipulation">trade-based manipulation</a> problem, where traders attempt to manipulate <a href="/wiki/Stock_price" class="mw-redirect" title="Stock price">stock prices</a> by buying and selling activities, ensemble classifiers are required to analyze the changes in the <a href="/wiki/Stock_market" title="Stock market">stock market</a> data and detect suspicious symptom of <a href="/wiki/Stock_price" class="mw-redirect" title="Stock price">stock price</a> <a href="/wiki/Market_manipulation" title="Market manipulation">manipulation</a>.<sup id="cite_ref-ReferenceA_79-1" class="reference"><a href="#cite_note-ReferenceA-79"><span class="cite-bracket">[</span>79<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Medicine">Medicine</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=28" title="Edit section: Medicine"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Ensemble classifiers have been successfully applied in <a href="/wiki/Neuroscience" title="Neuroscience">neuroscience</a>, <a href="/wiki/Proteomics" title="Proteomics">proteomics</a> and <a href="/wiki/Medical_diagnosis" title="Medical diagnosis">medical diagnosis</a> like in <a href="/wiki/Neurocognitive" class="mw-redirect" title="Neurocognitive">neuro-cognitive disorder</a> (i.e. <a href="/wiki/Alzheimer" class="mw-redirect" title="Alzheimer">Alzheimer</a> or <a href="/wiki/Myotonic_dystrophy" title="Myotonic dystrophy">myotonic dystrophy</a>) detection based on MRI datasets,<sup id="cite_ref-80" class="reference"><a href="#cite_note-80"><span class="cite-bracket">[</span>80<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-81" class="reference"><a href="#cite_note-81"><span class="cite-bracket">[</span>81<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-82" class="reference"><a href="#cite_note-82"><span class="cite-bracket">[</span>82<span class="cite-bracket">]</span></a></sup> and cervical cytology classification.<sup id="cite_ref-83" class="reference"><a href="#cite_note-83"><span class="cite-bracket">[</span>83<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-84" class="reference"><a href="#cite_note-84"><span class="cite-bracket">[</span>84<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading2"><h2 id="See_also">See also</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=29" title="Edit section: See also"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li><a href="/wiki/Ensemble_averaging_(machine_learning)" title="Ensemble averaging (machine learning)">Ensemble averaging (machine learning)</a></li> <li><a href="/wiki/Bayesian_structural_time_series" title="Bayesian structural time series">Bayesian structural time series</a> (BSTS)</li></ul> <div class="mw-heading mw-heading2"><h2 id="References">References</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=30" title="Edit section: References"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <style data-mw-deduplicate="TemplateStyles:r1239543626">.mw-parser-output .reflist{margin-bottom:0.5em;list-style-type:decimal}@media screen{.mw-parser-output .reflist{font-size:90%}}.mw-parser-output .reflist .references{font-size:100%;margin-bottom:0;list-style-type:inherit}.mw-parser-output .reflist-columns-2{column-width:30em}.mw-parser-output .reflist-columns-3{column-width:25em}.mw-parser-output .reflist-columns{margin-top:0.3em}.mw-parser-output .reflist-columns ol{margin-top:0}.mw-parser-output .reflist-columns li{page-break-inside:avoid;break-inside:avoid-column}.mw-parser-output .reflist-upper-alpha{list-style-type:upper-alpha}.mw-parser-output .reflist-upper-roman{list-style-type:upper-roman}.mw-parser-output .reflist-lower-alpha{list-style-type:lower-alpha}.mw-parser-output .reflist-lower-greek{list-style-type:lower-greek}.mw-parser-output .reflist-lower-roman{list-style-type:lower-roman}</style><div class="reflist reflist-columns references-column-width" style="column-width: 30em;"> <ol class="references"> <li id="cite_note-1"><span class="mw-cite-backlink"><b><a href="#cite_ref-1">^</a></b></span> <span class="reference-text"><style data-mw-deduplicate="TemplateStyles:r1238218222">.mw-parser-output cite.citation{font-style:inherit;word-wrap:break-word}.mw-parser-output .citation q{quotes:"\"""\"""'""'"}.mw-parser-output .citation:target{background-color:rgba(0,127,255,0.133)}.mw-parser-output .id-lock-free.id-lock-free a{background:url("//upload.wikimedia.org/wikipedia/commons/6/65/Lock-green.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-limited.id-lock-limited a,.mw-parser-output .id-lock-registration.id-lock-registration a{background:url("//upload.wikimedia.org/wikipedia/commons/d/d6/Lock-gray-alt-2.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-subscription.id-lock-subscription a{background:url("//upload.wikimedia.org/wikipedia/commons/a/aa/Lock-red-alt-2.svg")right 0.1em center/9px no-repeat}.mw-parser-output .cs1-ws-icon a{background:url("//upload.wikimedia.org/wikipedia/commons/4/4c/Wikisource-logo.svg")right 0.1em center/12px no-repeat}body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-free a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-limited a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-registration a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-subscription a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .cs1-ws-icon a{background-size:contain;padding:0 1em 0 0}.mw-parser-output .cs1-code{color:inherit;background:inherit;border:none;padding:inherit}.mw-parser-output .cs1-hidden-error{display:none;color:var(--color-error,#d33)}.mw-parser-output .cs1-visible-error{color:var(--color-error,#d33)}.mw-parser-output .cs1-maint{display:none;color:#085;margin-left:0.3em}.mw-parser-output .cs1-kern-left{padding-left:0.2em}.mw-parser-output .cs1-kern-right{padding-right:0.2em}.mw-parser-output .citation .mw-selflink{font-weight:inherit}@media screen{.mw-parser-output .cs1-format{font-size:95%}html.skin-theme-clientpref-night .mw-parser-output .cs1-maint{color:#18911f}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .cs1-maint{color:#18911f}}</style><cite id="CITEREFOpitzMaclin1999" class="citation journal cs1">Opitz, D.; Maclin, R. (1999). <a rel="nofollow" class="external text" href="https://doi.org/10.1613%2Fjair.614">"Popular ensemble methods: An empirical study"</a>. <i><a href="/wiki/Journal_of_Artificial_Intelligence_Research" title="Journal of Artificial Intelligence Research">Journal of Artificial Intelligence Research</a></i>. <b>11</b>: 169–198. <a href="/wiki/ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://arxiv.org/abs/1106.0257">1106.0257</a></span>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1613%2Fjair.614">10.1613/jair.614</a></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Journal+of+Artificial+Intelligence+Research&rft.atitle=Popular+ensemble+methods%3A+An+empirical+study&rft.volume=11&rft.pages=169-198&rft.date=1999&rft_id=info%3Aarxiv%2F1106.0257&rft_id=info%3Adoi%2F10.1613%2Fjair.614&rft.aulast=Opitz&rft.aufirst=D.&rft.au=Maclin%2C+R.&rft_id=https%3A%2F%2Fdoi.org%2F10.1613%252Fjair.614&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-2"><span class="mw-cite-backlink"><b><a href="#cite_ref-2">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFPolikar2006" class="citation journal cs1">Polikar, R. (2006). "Ensemble based systems in decision making". <i>IEEE Circuits and Systems Magazine</i>. <b>6</b> (3): 21–45. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1109%2FMCAS.2006.1688199">10.1109/MCAS.2006.1688199</a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:18032543">18032543</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=IEEE+Circuits+and+Systems+Magazine&rft.atitle=Ensemble+based+systems+in+decision+making&rft.volume=6&rft.issue=3&rft.pages=21-45&rft.date=2006&rft_id=info%3Adoi%2F10.1109%2FMCAS.2006.1688199&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A18032543%23id-name%3DS2CID&rft.aulast=Polikar&rft.aufirst=R.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-Rokach2010-3"><span class="mw-cite-backlink">^ <a href="#cite_ref-Rokach2010_3-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-Rokach2010_3-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFRokach2010" class="citation journal cs1">Rokach, L. (2010). "Ensemble-based classifiers". <i>Artificial Intelligence Review</i>. <b>33</b> (1–2): 1–39. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1007%2Fs10462-009-9124-7">10.1007/s10462-009-9124-7</a>. <a href="/wiki/Hdl_(identifier)" class="mw-redirect" title="Hdl (identifier)">hdl</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://hdl.handle.net/11323%2F1748">11323/1748</a></span>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:11149239">11149239</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Artificial+Intelligence+Review&rft.atitle=Ensemble-based+classifiers&rft.volume=33&rft.issue=1%E2%80%932&rft.pages=1-39&rft.date=2010&rft_id=info%3Ahdl%2F11323%2F1748&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A11149239%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1007%2Fs10462-009-9124-7&rft.aulast=Rokach&rft.aufirst=L.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-4"><span class="mw-cite-backlink"><b><a href="#cite_ref-4">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFBlockeel_H.2011" class="citation book cs1">Blockeel H. (2011). <a rel="nofollow" class="external text" href="https://lirias.kuleuven.be/handle/123456789/298291">"Hypothesis Space"</a>. <i>Encyclopedia of Machine Learning</i>. pp. 511–513. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1007%2F978-0-387-30164-8_373">10.1007/978-0-387-30164-8_373</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-387-30768-8" title="Special:BookSources/978-0-387-30768-8"><bdi>978-0-387-30768-8</bdi></a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=Hypothesis+Space&rft.btitle=Encyclopedia+of+Machine+Learning&rft.pages=511-513&rft.date=2011&rft_id=info%3Adoi%2F10.1007%2F978-0-387-30164-8_373&rft.isbn=978-0-387-30768-8&rft.au=Blockeel+H.&rft_id=https%3A%2F%2Flirias.kuleuven.be%2Fhandle%2F123456789%2F298291&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-5"><span class="mw-cite-backlink"><b><a href="#cite_ref-5">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFIbomoiye_Domor_Mienye,_Yanxia_Sun2022" class="citation book cs1">Ibomoiye Domor Mienye, Yanxia Sun (2022). <a rel="nofollow" class="external text" href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=survey+of+ensemble+learning&oq=survey+of+ensemble#d=gs_qabs&t=1711172531516&u=%23p%3DONpJl2uwB3UJ"><i>A Survey of Ensemble Learning: Concepts, Algorithms, Applications and Prospects</i></a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=A+Survey+of+Ensemble+Learning%3A+Concepts%2C+Algorithms%2C+Applications+and+Prospects&rft.date=2022&rft.au=Ibomoiye+Domor+Mienye%2C+Yanxia+Sun&rft_id=https%3A%2F%2Fscholar.google.com%2Fscholar%3Fhl%3Den%26as_sdt%3D0%252C5%26q%3Dsurvey%2Bof%2Bensemble%2Blearning%26oq%3Dsurvey%2Bof%2Bensemble%23d%3Dgs_qabs%26t%3D1711172531516%26u%3D%2523p%253DONpJl2uwB3UJ&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-6"><span class="mw-cite-backlink"><b><a href="#cite_ref-6">^</a></b></span> <span class="reference-text"><a href="/wiki/Ludmila_Kuncheva" title="Ludmila Kuncheva">Kuncheva, L.</a> and Whitaker, C., <a rel="nofollow" class="external text" href="https://link.springer.com/content/pdf/10.1023/A:1022859003006.pdf">Measures of diversity in classifier ensembles</a>, <i>Machine Learning</i>, 51, pp. 181-207, 2003</span> </li> <li id="cite_note-7"><span class="mw-cite-backlink"><b><a href="#cite_ref-7">^</a></b></span> <span class="reference-text">Sollich, P. and Krogh, A., <a rel="nofollow" class="external text" href="https://proceedings.neurips.cc/paper/1995/file/1019c8091693ef5c5f55970346633f92-Paper.pdf"><i>Learning with ensembles: How overfitting can be useful</i></a>, Advances in Neural Information Processing Systems, volume 8, pp. 190-196, 1996.</span> </li> <li id="cite_note-8"><span class="mw-cite-backlink"><b><a href="#cite_ref-8">^</a></b></span> <span class="reference-text">Brown, G. and Wyatt, J. and Harris, R. and Yao, X., <a rel="nofollow" class="external text" href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.421.349&rep=rep1&type=pdf">Diversity creation methods: a survey and categorisation</a>., <i>Information Fusion</i>, 6(1), pp.5-20, 2005.</span> </li> <li id="cite_note-9"><span class="mw-cite-backlink"><b><a href="#cite_ref-9">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFAdevaCerviñoCalvo2005" class="citation journal cs1">Adeva, J. J. García; Cerviño, Ulises; Calvo, R. (December 2005). <a rel="nofollow" class="external text" href="https://www.clei.org/cleiej/index.php/cleiej/article/view/319/112">"Accuracy and Diversity in Ensembles of Text Categorisers"</a> <span class="cs1-format">(PDF)</span>. <i>CLEI Journal</i>. <b>8</b> (2): 1:1–1:12. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.19153%2Fcleiej.8.2.1">10.19153/cleiej.8.2.1</a></span> (inactive 1 November 2024).</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=CLEI+Journal&rft.atitle=Accuracy+and+Diversity+in+Ensembles+of+Text+Categorisers&rft.volume=8&rft.issue=2&rft.pages=1%3A1-1%3A12&rft.date=2005-12&rft_id=info%3Adoi%2F10.19153%2Fcleiej.8.2.1&rft.aulast=Adeva&rft.aufirst=J.+J.+Garc%C3%ADa&rft.au=Cervi%C3%B1o%2C+Ulises&rft.au=Calvo%2C+R.&rft_id=https%3A%2F%2Fwww.clei.org%2Fcleiej%2Findex.php%2Fcleiej%2Farticle%2Fview%2F319%2F112&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span><span class="cs1-maint citation-comment"><code class="cs1-code">{{<a href="/wiki/Template:Cite_journal" title="Template:Cite journal">cite journal</a>}}</code>: CS1 maint: DOI inactive as of November 2024 (<a href="/wiki/Category:CS1_maint:_DOI_inactive_as_of_November_2024" title="Category:CS1 maint: DOI inactive as of November 2024">link</a>)</span></span> </li> <li id="cite_note-10"><span class="mw-cite-backlink"><b><a href="#cite_ref-10">^</a></b></span> <span class="reference-text">Ho, T., Random Decision Forests, <i>Proceedings of the Third International Conference on Document Analysis and Recognition</i>, pp. 278-282, 1995.</span> </li> <li id="cite_note-11"><span class="mw-cite-backlink"><b><a href="#cite_ref-11">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFGashlerGiraud-CarrierMartinez2008" class="citation book cs1">Gashler, M.; Giraud-Carrier, C.; Martinez, T. (2008). <a rel="nofollow" class="external text" href="http://axon.cs.byu.edu/papers/gashler2008icmla.pdf">"Decision Tree Ensemble: Small Heterogeneous is Better Than Large Homogeneous"</a> <span class="cs1-format">(PDF)</span>. <a rel="nofollow" class="external text" href="https://scholarsarchive.byu.edu/facpub/902"><i>2008 Seventh International Conference on Machine Learning and Applications</i></a>. Vol. 2008. pp. 900–905. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1109%2FICMLA.2008.154">10.1109/ICMLA.2008.154</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-7695-3495-4" title="Special:BookSources/978-0-7695-3495-4"><bdi>978-0-7695-3495-4</bdi></a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:614810">614810</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=Decision+Tree+Ensemble%3A+Small+Heterogeneous+is+Better+Than+Large+Homogeneous&rft.btitle=2008+Seventh+International+Conference+on+Machine+Learning+and+Applications&rft.pages=900-905&rft.date=2008&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A614810%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1109%2FICMLA.2008.154&rft.isbn=978-0-7695-3495-4&rft.aulast=Gashler&rft.aufirst=M.&rft.au=Giraud-Carrier%2C+C.&rft.au=Martinez%2C+T.&rft_id=http%3A%2F%2Faxon.cs.byu.edu%2Fpapers%2Fgashler2008icmla.pdf&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-12"><span class="mw-cite-backlink"><b><a href="#cite_ref-12">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFLiuYao1999" class="citation journal cs1">Liu, Y.; Yao, X. (December 1999). <a rel="nofollow" class="external text" href="https://doi.org/10.1016/S0893-6080(99)00073-8">"Ensemble learning via negative correlation"</a>. <i>Neural Networks</i>. <b>12</b> (10): 1399–1404. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2FS0893-6080%2899%2900073-8">10.1016/S0893-6080(99)00073-8</a>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/0893-6080">0893-6080</a>. <a href="/wiki/PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/12662623">12662623</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Neural+Networks&rft.atitle=Ensemble+learning+via+negative+correlation&rft.volume=12&rft.issue=10&rft.pages=1399-1404&rft.date=1999-12&rft.issn=0893-6080&rft_id=info%3Apmid%2F12662623&rft_id=info%3Adoi%2F10.1016%2FS0893-6080%2899%2900073-8&rft.aulast=Liu&rft.aufirst=Y.&rft.au=Yao%2C+X.&rft_id=https%3A%2F%2Fdoi.org%2F10.1016%2FS0893-6080%2899%2900073-8&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-13"><span class="mw-cite-backlink"><b><a href="#cite_ref-13">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFShohamPermuter2019" class="citation book cs1">Shoham, Ron; Permuter, Haim (2019). "Amended Cross-Entropy Cost: An Approach for Encouraging Diversity in Classification Ensemble (Brief Announcement)". <i>Cyber Security Cryptography and Machine Learning</i>. Lecture Notes in Computer Science. Vol. 11527. pp. 202–207. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1007%2F978-3-030-20951-3_18">10.1007/978-3-030-20951-3_18</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-3-030-20950-6" title="Special:BookSources/978-3-030-20950-6"><bdi>978-3-030-20950-6</bdi></a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:189926552">189926552</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=Amended+Cross-Entropy+Cost%3A+An+Approach+for+Encouraging+Diversity+in+Classification+Ensemble+%28Brief+Announcement%29&rft.btitle=Cyber+Security+Cryptography+and+Machine+Learning&rft.series=Lecture+Notes+in+Computer+Science&rft.pages=202-207&rft.date=2019&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A189926552%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1007%2F978-3-030-20951-3_18&rft.isbn=978-3-030-20950-6&rft.aulast=Shoham&rft.aufirst=Ron&rft.au=Permuter%2C+Haim&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-14"><span class="mw-cite-backlink"><b><a href="#cite_ref-14">^</a></b></span> <span class="reference-text">Terufumi Morishita et al, <a rel="nofollow" class="external text" href="https://proceedings.mlr.press/v162/morishita22a.html"><i>Rethinking Fano’s Inequality in Ensemble Learning</i></a>, International Conference on Machine Learning, 2022</span> </li> <li id="cite_note-15"><span class="mw-cite-backlink"><b><a href="#cite_ref-15">^</a></b></span> <span class="reference-text">Wu, S., Li, J., & Ding, W. (2023) A geometric framework for multiclass ensemble classifiers, <i>Machine Learning</i>, 112(12), pp. 4929-4958. <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1007%2FS10994-023-06406-W">10.1007/S10994-023-06406-W</a></span> </li> <li id="cite_note-16"><span class="mw-cite-backlink"><b><a href="#cite_ref-16">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFR._BonabCan2016" class="citation conference cs1">R. Bonab, Hamed; Can, Fazli (2016). <a rel="nofollow" class="external text" href="http://dl.acm.org/citation.cfm?id=2983907"><i>A Theoretical Framework on the Ideal Number of Classifiers for Online Ensembles in Data Streams</i></a>. CIKM. USA: ACM. p. 2053.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=conference&rft.btitle=A+Theoretical+Framework+on+the+Ideal+Number+of+Classifiers+for+Online+Ensembles+in+Data+Streams&rft.place=USA&rft.pages=2053&rft.pub=ACM&rft.date=2016&rft.aulast=R.+Bonab&rft.aufirst=Hamed&rft.au=Can%2C+Fazli&rft_id=http%3A%2F%2Fdl.acm.org%2Fcitation.cfm%3Fid%3D2983907&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-17"><span class="mw-cite-backlink"><b><a href="#cite_ref-17">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFBonabCan2017" class="citation arxiv cs1">Bonab, Hamed; Can, Fazli (2017). "Less is More: A Comprehensive Framework for the Number of Components of Ensemble Classifiers". <a href="/wiki/ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://arxiv.org/abs/1709.02925">1709.02925</a></span> [<a rel="nofollow" class="external text" href="https://arxiv.org/archive/cs.LG">cs.LG</a>].</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=preprint&rft.jtitle=arXiv&rft.atitle=Less+is+More%3A+A+Comprehensive+Framework+for+the+Number+of+Components+of+Ensemble+Classifiers&rft.date=2017&rft_id=info%3Aarxiv%2F1709.02925&rft.aulast=Bonab&rft.aufirst=Hamed&rft.au=Can%2C+Fazli&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-18"><span class="mw-cite-backlink"><b><a href="#cite_ref-18">^</a></b></span> <span class="reference-text"><a href="/wiki/Tom_M._Mitchell" title="Tom M. Mitchell">Tom M. Mitchell</a>, <i>Machine Learning</i>, 1997, pp. 175</span> </li> <li id="cite_note-19"><span class="mw-cite-backlink"><b><a href="#cite_ref-19">^</a></b></span> <span class="reference-text">Salman, R., Alzaatreh, A., Sulieman, H., & Faisal, S. (2021). A Bootstrap Framework for Aggregating within and between Feature Selection Methods. Entropy (Basel, Switzerland), 23(2), 200. <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.3390%2Fe23020200">10.3390/e23020200</a></span> </li> <li id="cite_note-20"><span class="mw-cite-backlink"><b><a href="#cite_ref-20">^</a></b></span> <span class="reference-text">Breiman, L., Bagging Predictors, <i>Machine Learning</i>, 24(2), pp.123-140, 1996. <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1007%2FBF00058655">10.1007/BF00058655</a></span> </li> <li id="cite_note-21"><span class="mw-cite-backlink"><b><a href="#cite_ref-21">^</a></b></span> <span class="reference-text">Brodeur, Z. P., Herman, J. D., & Steinschneider, S. (2020). Bootstrap aggregation and cross-validation methods to reduce overfitting in reservoir control policy search. Water Resources Research, 56, e2020WR027184. <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1029%2F2020WR027184">10.1029/2020WR027184</a></span> </li> <li id="cite_note-22"><span class="mw-cite-backlink"><b><a href="#cite_ref-22">^</a></b></span> <span class="reference-text">e.g., <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFJennifer_A._HoetingDavid_MadiganAdrian_RafteryChris_Volinsky1999" class="citation journal cs1"><a href="/wiki/Jennifer_A._Hoeting" title="Jennifer A. Hoeting">Jennifer A. Hoeting</a>; <a href="/wiki/David_Madigan" title="David Madigan">David Madigan</a>; <a href="/wiki/Adrian_Raftery" title="Adrian Raftery">Adrian Raftery</a>; Chris Volinsky (1999). <a rel="nofollow" class="external text" href="https://projecteuclid.org/euclid.ss/1009212519">"Bayesian Model Averaging: A Tutorial"</a>. <i><a href="/wiki/Statistical_Science" title="Statistical Science">Statistical Science</a></i>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/0883-4237">0883-4237</a>. <a href="/wiki/WDQ_(identifier)" class="mw-redirect" title="WDQ (identifier)">Wikidata</a> <a href="https://www.wikidata.org/wiki/Q98974344" class="extiw" title="d:Q98974344">Q98974344</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Statistical+Science&rft.atitle=Bayesian+Model+Averaging%3A+A+Tutorial&rft.date=1999&rft.issn=0883-4237&rft.au=Jennifer+A.+Hoeting&rft.au=David+Madigan&rft.au=Adrian+Raftery&rft.au=Chris+Volinsky&rft_id=https%3A%2F%2Fprojecteuclid.org%2Feuclid.ss%2F1009212519&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-23"><span class="mw-cite-backlink"><b><a href="#cite_ref-23">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFChris_FraleyAdrian_RafteryJ._McLean_SloughterTilmann_Gneiting" class="citation cs2">Chris Fraley; <a href="/wiki/Adrian_Raftery" title="Adrian Raftery">Adrian Raftery</a>; J. McLean Sloughter; Tilmann Gneiting, <i>ensembleBMA: Probabilistic Forecasting using Ensembles and Bayesian Model Averaging</i>, <a href="/wiki/WDQ_(identifier)" class="mw-redirect" title="WDQ (identifier)">Wikidata</a> <a href="https://www.wikidata.org/wiki/Q98972500" class="extiw" title="d:Q98972500">Q98972500</a></cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=ensembleBMA%3A+Probabilistic+Forecasting+using+Ensembles+and+Bayesian+Model+Averaging&rft.au=Chris+Fraley&rft.au=Adrian+Raftery&rft.au=J.+McLean+Sloughter&rft.au=Tilmann+Gneiting&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-24"><span class="mw-cite-backlink"><b><a href="#cite_ref-24">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFAdrian_RafteryJennifer_A._HoetingChris_VolinskyIan_Painter" class="citation cs2"><a href="/wiki/Adrian_Raftery" title="Adrian Raftery">Adrian Raftery</a>; <a href="/wiki/Jennifer_A._Hoeting" title="Jennifer A. Hoeting">Jennifer A. Hoeting</a>; Chris Volinsky; Ian Painter; Ka Yee Yeung, <a rel="nofollow" class="external text" href="https://github.com/hanase/BMA"><i>BMA: Bayesian Model Averaging</i></a>, <a href="/wiki/WDQ_(identifier)" class="mw-redirect" title="WDQ (identifier)">Wikidata</a> <a href="https://www.wikidata.org/wiki/Q91674106" class="extiw" title="d:Q91674106">Q91674106</a></cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=BMA%3A+Bayesian+Model+Averaging&rft.au=Adrian+Raftery&rft.au=Jennifer+A.+Hoeting&rft.au=Chris+Volinsky&rft.au=Ian+Painter&rft.au=Ka+Yee+Yeung&rft_id=https%3A%2F%2Fgithub.com%2Fhanase%2FBMA&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span>.</span> </li> <li id="cite_note-25"><span class="mw-cite-backlink"><b><a href="#cite_ref-25">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFAdrian_Raftery1995" class="citation journal cs1"><a href="/wiki/Adrian_Raftery" title="Adrian Raftery">Adrian Raftery</a> (1995). "Bayesian model selection in social research". <i><a href="/wiki/Sociological_Methodology" title="Sociological Methodology">Sociological Methodology</a></i>: 111–196. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.2307%2F271063">10.2307/271063</a>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/0081-1750">0081-1750</a>. <a href="/wiki/WDQ_(identifier)" class="mw-redirect" title="WDQ (identifier)">Wikidata</a> <a href="https://www.wikidata.org/wiki/Q91670340" class="extiw" title="d:Q91670340">Q91670340</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Sociological+Methodology&rft.atitle=Bayesian+model+selection+in+social+research&rft.pages=111-196&rft.date=1995&rft_id=info%3Adoi%2F10.2307%2F271063&rft.issn=0081-1750&rft.au=Adrian+Raftery&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-26"><span class="mw-cite-backlink"><b><a href="#cite_ref-26">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFMerlise_A._ClydeMichael_L._LittmanQuanli_WangJoyee_Ghosh" class="citation cs2"><a href="/wiki/Merlise_A._Clyde" title="Merlise A. Clyde">Merlise A. Clyde</a>; <a href="/wiki/Michael_L._Littman" title="Michael L. Littman">Michael L. Littman</a>; Quanli Wang; Joyee Ghosh; Yingbo Li; Don van den Bergh, <i>BAS: Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling</i>, <a href="/wiki/WDQ_(identifier)" class="mw-redirect" title="WDQ (identifier)">Wikidata</a> <a href="https://www.wikidata.org/wiki/Q98974089" class="extiw" title="d:Q98974089">Q98974089</a></cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=BAS%3A+Bayesian+Variable+Selection+and+Model+Averaging+using+Bayesian+Adaptive+Sampling&rft.au=Merlise+A.+Clyde&rft.au=Michael+L.+Littman&rft.au=Quanli+Wang&rft.au=Joyee+Ghosh&rft.au=Yingbo+Li&rft.au=Don+van+den+Bergh&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span>.</span> </li> <li id="cite_note-27"><span class="mw-cite-backlink"><b><a href="#cite_ref-27">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFGerda_ClaeskensNils_Lid_Hjort2008" class="citation cs2"><a href="/wiki/Gerda_Claeskens" title="Gerda Claeskens">Gerda Claeskens</a>; <a href="/wiki/Nils_Lid_Hjort" title="Nils Lid Hjort">Nils Lid Hjort</a> (2008), <i>Model selection and model averaging</i>, <a href="/wiki/Cambridge_University_Press" title="Cambridge University Press">Cambridge University Press</a>, <a href="/wiki/WDQ_(identifier)" class="mw-redirect" title="WDQ (identifier)">Wikidata</a> <a href="https://www.wikidata.org/wiki/Q62568358" class="extiw" title="d:Q62568358">Q62568358</a></cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=Model+selection+and+model+averaging&rft.pub=Cambridge+University+Press&rft.date=2008&rft.au=Gerda+Claeskens&rft.au=Nils+Lid+Hjort&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span>, ch. 4.</span> </li> <li id="cite_note-28"><span class="mw-cite-backlink"><b><a href="#cite_ref-28">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFHausslerKearnsSchapire1994" class="citation journal cs1">Haussler, David; Kearns, Michael; Schapire, Robert E. (1994). <a rel="nofollow" class="external text" href="https://doi.org/10.1007%2Fbf00993163">"Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension"</a>. <i>Machine Learning</i>. <b>14</b>: 83–113. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1007%2Fbf00993163">10.1007/bf00993163</a></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Machine+Learning&rft.atitle=Bounds+on+the+sample+complexity+of+Bayesian+learning+using+information+theory+and+the+VC+dimension&rft.volume=14&rft.pages=83-113&rft.date=1994&rft_id=info%3Adoi%2F10.1007%2Fbf00993163&rft.aulast=Haussler&rft.aufirst=David&rft.au=Kearns%2C+Michael&rft.au=Schapire%2C+Robert+E.&rft_id=https%3A%2F%2Fdoi.org%2F10.1007%252Fbf00993163&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-29"><span class="mw-cite-backlink"><b><a href="#cite_ref-29">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFKenneth_P._BurnhamDavid_R._Anderson1998" class="citation cs2">Kenneth P. Burnham; David R. Anderson (1998), <i>Model Selection and Inference: A practical information-theoretic approach</i>, <a href="/wiki/Springer_Science%2BBusiness_Media" title="Springer Science+Business Media">Springer Science+Business Media</a>, <a href="/wiki/WDQ_(identifier)" class="mw-redirect" title="WDQ (identifier)">Wikidata</a> <a href="https://www.wikidata.org/wiki/Q62670082" class="extiw" title="d:Q62670082">Q62670082</a></cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=Model+Selection+and+Inference%3A+A+practical+information-theoretic+approach&rft.pub=Springer+Science%2BBusiness+Media&rft.date=1998&rft.au=Kenneth+P.+Burnham&rft.au=David+R.+Anderson&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span> and <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFKenneth_P._BurnhamDavid_R._Anderson2002" class="citation cs2">Kenneth P. Burnham; David R. Anderson (2002), <i>Model Selection and Multimodel Inference: A practical information-theoretic approach</i>, <a href="/wiki/Springer_Science%2BBusiness_Media" title="Springer Science+Business Media">Springer Science+Business Media</a>, <a href="/wiki/WDQ_(identifier)" class="mw-redirect" title="WDQ (identifier)">Wikidata</a> <a href="https://www.wikidata.org/wiki/Q76889160" class="extiw" title="d:Q76889160">Q76889160</a></cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=Model+Selection+and+Multimodel+Inference%3A+A+practical+information-theoretic+approach&rft.pub=Springer+Science%2BBusiness+Media&rft.date=2002&rft.au=Kenneth+P.+Burnham&rft.au=David+R.+Anderson&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span>.</span> </li> <li id="cite_note-30"><span class="mw-cite-backlink"><b><a href="#cite_ref-30">^</a></b></span> <span class="reference-text">The Wikiversity article on <a href="https://en.wikiversity.org/wiki/Searching_R_Packages" class="extiw" title="v:Searching R Packages">Searching R Packages</a> mentions several ways to find available packages for something like this. For example, "sos::findFn('{Bayesian model averaging}')" from within R will search for help files in contributed packages that includes the search term and open two tabs in the default browser. The first will list all the help files found sorted by package. The second summarizes the packages found, sorted by the apparent strength of the match.</span> </li> <li id="cite_note-31"><span class="mw-cite-backlink"><b><a href="#cite_ref-31">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFMonteith,_KristineCarroll,_JamesSeppi,_KevinMartinez,_Tony.2011" class="citation conference cs1">Monteith, Kristine; Carroll, James; Seppi, Kevin; Martinez, Tony. (2011). <a rel="nofollow" class="external text" href="http://axon.cs.byu.edu/papers/Kristine.ijcnn2011.pdf"><i>Turning Bayesian Model Averaging into Bayesian Model Combination</i></a> <span class="cs1-format">(PDF)</span>. Proceedings of the International Joint Conference on Neural Networks IJCNN'11. pp. 2657–2663.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=conference&rft.btitle=Turning+Bayesian+Model+Averaging+into+Bayesian+Model+Combination&rft.pages=2657-2663&rft.date=2011&rft.au=Monteith%2C+Kristine&rft.au=Carroll%2C+James&rft.au=Seppi%2C+Kevin&rft.au=Martinez%2C+Tony.&rft_id=http%3A%2F%2Faxon.cs.byu.edu%2Fpapers%2FKristine.ijcnn2011.pdf&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-32"><span class="mw-cite-backlink"><b><a href="#cite_ref-32">^</a></b></span> <span class="reference-text">Saso Dzeroski, Bernard Zenko, <i><a rel="nofollow" class="external text" href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.108.6096">Is Combining Classifiers Better than Selecting the Best One</a></i>, Machine Learning, 2004, pp. 255-273</span> </li> <li id="cite_note-33"><span class="mw-cite-backlink"><b><a href="#cite_ref-33">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFBensusanGiraud-Carrier2000" class="citation book cs1">Bensusan, Hilan; Giraud-Carrier, Christophe (2000). <a rel="nofollow" class="external text" href="https://link.springer.com/content/pdf/10.1007/3-540-45372-5_32.pdf">"Discovering Task Neighbourhoods through Landmark Learning Performances"</a> <span class="cs1-format">(PDF)</span>. <i>Principles of Data Mining and Knowledge Discovery</i>. Lecture Notes in Computer Science. Vol. 1910. pp. 325–330. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1007%2F3-540-45372-5_32">10.1007/3-540-45372-5_32</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-3-540-41066-9" title="Special:BookSources/978-3-540-41066-9"><bdi>978-3-540-41066-9</bdi></a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=Discovering+Task+Neighbourhoods+through+Landmark+Learning+Performances&rft.btitle=Principles+of+Data+Mining+and+Knowledge+Discovery&rft.series=Lecture+Notes+in+Computer+Science&rft.pages=325-330&rft.date=2000&rft_id=info%3Adoi%2F10.1007%2F3-540-45372-5_32&rft.isbn=978-3-540-41066-9&rft.aulast=Bensusan&rft.aufirst=Hilan&rft.au=Giraud-Carrier%2C+Christophe&rft_id=https%3A%2F%2Flink.springer.com%2Fcontent%2Fpdf%2F10.1007%2F3-540-45372-5_32.pdf&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-34"><span class="mw-cite-backlink"><b><a href="#cite_ref-34">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFShohamPermuter2019" class="citation book cs1">Shoham, Ron; Permuter, Haim (2019). "Amended Cross-Entropy Cost: An Approach for Encouraging Diversity in Classification Ensemble (Brief Announcement)". <i>Cyber Security Cryptography and Machine Learning</i>. Lecture Notes in Computer Science. Vol. 11527. pp. 202–207. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1007%2F978-3-030-20951-3_18">10.1007/978-3-030-20951-3_18</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-3-030-20950-6" title="Special:BookSources/978-3-030-20950-6"><bdi>978-3-030-20950-6</bdi></a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=Amended+Cross-Entropy+Cost%3A+An+Approach+for+Encouraging+Diversity+in+Classification+Ensemble+%28Brief+Announcement%29&rft.btitle=Cyber+Security+Cryptography+and+Machine+Learning&rft.series=Lecture+Notes+in+Computer+Science&rft.pages=202-207&rft.date=2019&rft_id=info%3Adoi%2F10.1007%2F978-3-030-20951-3_18&rft.isbn=978-3-030-20950-6&rft.aulast=Shoham&rft.aufirst=Ron&rft.au=Permuter%2C+Haim&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-35"><span class="mw-cite-backlink"><b><a href="#cite_ref-35">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFShohamPermuter2020" class="citation arxiv cs1">Shoham, Ron; Permuter, Haim (2020). "Amended Cross Entropy Cost: Framework For Explicit Diversity Encouragement". <a href="/wiki/ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://arxiv.org/abs/2007.08140">2007.08140</a></span> [<a rel="nofollow" class="external text" href="https://arxiv.org/archive/cs.LG">cs.LG</a>].</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=preprint&rft.jtitle=arXiv&rft.atitle=Amended+Cross+Entropy+Cost%3A+Framework+For+Explicit+Diversity+Encouragement&rft.date=2020&rft_id=info%3Aarxiv%2F2007.08140&rft.aulast=Shoham&rft.aufirst=Ron&rft.au=Permuter%2C+Haim&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-36"><span class="mw-cite-backlink"><b><a href="#cite_ref-36">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://scikit-learn.org/stable/modules/ensemble.html#stacking">"1.11. Ensemble methods"</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=unknown&rft.btitle=1.11.+Ensemble+methods&rft_id=https%3A%2F%2Fscikit-learn.org%2Fstable%2Fmodules%2Fensemble.html%23stacking&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-37"><span class="mw-cite-backlink"><b><a href="#cite_ref-37">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFWolpert1992" class="citation journal cs1">Wolpert (1992). "Stacked Generalization". <i>Neural Networks</i>. <b>5</b> (2): 241–259. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fs0893-6080%2805%2980023-1">10.1016/s0893-6080(05)80023-1</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Neural+Networks&rft.atitle=Stacked+Generalization.&rft.volume=5&rft.issue=2&rft.pages=241-259&rft.date=1992&rft_id=info%3Adoi%2F10.1016%2Fs0893-6080%2805%2980023-1&rft.au=Wolpert&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-38"><span class="mw-cite-backlink"><b><a href="#cite_ref-38">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFBreiman1996" class="citation journal cs1">Breiman, Leo (1996). <a rel="nofollow" class="external text" href="https://doi.org/10.1007%2FBF00117832">"Stacked regressions"</a>. <i>Machine Learning</i>. <b>24</b>: 49–64. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1007%2FBF00117832">10.1007/BF00117832</a></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Machine+Learning&rft.atitle=Stacked+regressions&rft.volume=24&rft.pages=49-64&rft.date=1996&rft_id=info%3Adoi%2F10.1007%2FBF00117832&rft.aulast=Breiman&rft.aufirst=Leo&rft_id=https%3A%2F%2Fdoi.org%2F10.1007%252FBF00117832&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-39"><span class="mw-cite-backlink"><b><a href="#cite_ref-39">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFOzayYarman_Vural2013" class="citation arxiv cs1">Ozay, M.; Yarman Vural, F. T. (2013). "A New Fuzzy Stacked Generalization Technique and Analysis of its Performance". <a href="/wiki/ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://arxiv.org/abs/1204.0171">1204.0171</a></span> [<a rel="nofollow" class="external text" href="https://arxiv.org/archive/cs.LG">cs.LG</a>].</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=preprint&rft.jtitle=arXiv&rft.atitle=A+New+Fuzzy+Stacked+Generalization+Technique+and+Analysis+of+its+Performance&rft.date=2013&rft_id=info%3Aarxiv%2F1204.0171&rft.aulast=Ozay&rft.aufirst=M.&rft.au=Yarman+Vural%2C+F.+T.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-40"><span class="mw-cite-backlink"><b><a href="#cite_ref-40">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFSmythWolpert1999" class="citation journal cs1">Smyth, Padhraic; Wolpert, David (1999). <a rel="nofollow" class="external text" href="https://link.springer.com/content/pdf/10.1023/A:1007511322260.pdf">"Linearly Combining Density Estimators via Stacking"</a> <span class="cs1-format">(PDF)</span>. <i>Machine Learning</i>. <b>36</b> (1): 59–83. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1023%2FA%3A1007511322260">10.1023/A:1007511322260</a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:16006860">16006860</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Machine+Learning&rft.atitle=Linearly+Combining+Density+Estimators+via+Stacking&rft.volume=36&rft.issue=1&rft.pages=59-83&rft.date=1999&rft_id=info%3Adoi%2F10.1023%2FA%3A1007511322260&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A16006860%23id-name%3DS2CID&rft.aulast=Smyth&rft.aufirst=Padhraic&rft.au=Wolpert%2C+David&rft_id=https%3A%2F%2Flink.springer.com%2Fcontent%2Fpdf%2F10.1023%2FA%3A1007511322260.pdf&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-41"><span class="mw-cite-backlink"><b><a href="#cite_ref-41">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFWolpertMacReady1999" class="citation journal cs1">Wolpert, David H.; MacReady, William G. (1999). <a rel="nofollow" class="external text" href="https://link.springer.com/content/pdf/10.1023/A:1007519102914.pdf">"An Efficient Method to Estimate Bagging's Generalization Error"</a> <span class="cs1-format">(PDF)</span>. <i>Machine Learning</i>. <b>35</b> (1): 41–55. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1023%2FA%3A1007519102914">10.1023/A:1007519102914</a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:14357246">14357246</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Machine+Learning&rft.atitle=An+Efficient+Method+to+Estimate+Bagging%27s+Generalization+Error&rft.volume=35&rft.issue=1&rft.pages=41-55&rft.date=1999&rft_id=info%3Adoi%2F10.1023%2FA%3A1007519102914&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A14357246%23id-name%3DS2CID&rft.aulast=Wolpert&rft.aufirst=David+H.&rft.au=MacReady%2C+William+G.&rft_id=https%3A%2F%2Flink.springer.com%2Fcontent%2Fpdf%2F10.1023%2FA%3A1007519102914.pdf&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-42"><span class="mw-cite-backlink"><b><a href="#cite_ref-42">^</a></b></span> <span class="reference-text">Clarke, B., <a rel="nofollow" class="external text" href="https://www.jmlr.org/papers/volume4/clarke03a/clarke03a.pdf"><i>Bayes model averaging and stacking when model approximation error cannot be ignored</i></a>, Journal of Machine Learning Research, pp 683-712, 2003</span> </li> <li id="cite_note-43"><span class="mw-cite-backlink"><b><a href="#cite_ref-43">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFSillTakacsMackeyLin2009" class="citation arxiv cs1">Sill, J.; Takacs, G.; Mackey, L.; Lin, D. (2009). "Feature-Weighted Linear Stacking". <a href="/wiki/ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://arxiv.org/abs/0911.0460">0911.0460</a></span> [<a rel="nofollow" class="external text" href="https://arxiv.org/archive/cs.LG">cs.LG</a>].</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=preprint&rft.jtitle=arXiv&rft.atitle=Feature-Weighted+Linear+Stacking&rft.date=2009&rft_id=info%3Aarxiv%2F0911.0460&rft.aulast=Sill&rft.aufirst=J.&rft.au=Takacs%2C+G.&rft.au=Mackey%2C+L.&rft.au=Lin%2C+D.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-44"><span class="mw-cite-backlink"><b><a href="#cite_ref-44">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFAminiParmeter2011" class="citation journal cs1">Amini, Shahram M.; Parmeter, Christopher F. (2011). <a rel="nofollow" class="external text" href="https://core.ac.uk/download/pdf/6494889.pdf">"Bayesian model averaging in R"</a> <span class="cs1-format">(PDF)</span>. <i>Journal of Economic and Social Measurement</i>. <b>36</b> (4): 253–287. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.3233%2FJEM-2011-0350">10.3233/JEM-2011-0350</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Journal+of+Economic+and+Social+Measurement&rft.atitle=Bayesian+model+averaging+in+R&rft.volume=36&rft.issue=4&rft.pages=253-287&rft.date=2011&rft_id=info%3Adoi%2F10.3233%2FJEM-2011-0350&rft.aulast=Amini&rft.aufirst=Shahram+M.&rft.au=Parmeter%2C+Christopher+F.&rft_id=https%3A%2F%2Fcore.ac.uk%2Fdownload%2Fpdf%2F6494889.pdf&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-45"><span class="mw-cite-backlink"><b><a href="#cite_ref-45">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://cran.r-project.org/web/packages/BMS/index.html">"BMS: Bayesian Model Averaging Library"</a>. <i>The Comprehensive R Archive Network</i>. 2015-11-24<span class="reference-accessdate">. Retrieved <span class="nowrap">September 9,</span> 2016</span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=The+Comprehensive+R+Archive+Network&rft.atitle=BMS%3A+Bayesian+Model+Averaging+Library&rft.date=2015-11-24&rft_id=https%3A%2F%2Fcran.r-project.org%2Fweb%2Fpackages%2FBMS%2Findex.html&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-46"><span class="mw-cite-backlink"><b><a href="#cite_ref-46">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://cran.r-project.org/web/packages/BAS/index.html">"BAS: Bayesian Model Averaging using Bayesian Adaptive Sampling"</a>. <i>The Comprehensive R Archive Network</i><span class="reference-accessdate">. Retrieved <span class="nowrap">September 9,</span> 2016</span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=The+Comprehensive+R+Archive+Network&rft.atitle=BAS%3A+Bayesian+Model+Averaging+using+Bayesian+Adaptive+Sampling&rft_id=https%3A%2F%2Fcran.r-project.org%2Fweb%2Fpackages%2FBAS%2Findex.html&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-47"><span class="mw-cite-backlink"><b><a href="#cite_ref-47">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://cran.r-project.org/web/packages/BMA/index.html">"BMA: Bayesian Model Averaging"</a>. <i>The Comprehensive R Archive Network</i><span class="reference-accessdate">. Retrieved <span class="nowrap">September 9,</span> 2016</span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=The+Comprehensive+R+Archive+Network&rft.atitle=BMA%3A+Bayesian+Model+Averaging&rft_id=https%3A%2F%2Fcran.r-project.org%2Fweb%2Fpackages%2FBMA%2Findex.html&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-48"><span class="mw-cite-backlink"><b><a href="#cite_ref-48">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://uk.mathworks.com/help/stats/classification-ensembles.html">"Classification Ensembles"</a>. <i>MATLAB & Simulink</i><span class="reference-accessdate">. Retrieved <span class="nowrap">June 8,</span> 2017</span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=MATLAB+%26+Simulink&rft.atitle=Classification+Ensembles&rft_id=https%3A%2F%2Fuk.mathworks.com%2Fhelp%2Fstats%2Fclassification-ensembles.html&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-s1-49"><span class="mw-cite-backlink">^ <a href="#cite_ref-s1_49-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-s1_49-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFWoźniakGrañaCorchado2014" class="citation journal cs1">Woźniak, Michał; Graña, Manuel; Corchado, Emilio (March 2014). "A survey of multiple classifier systems as hybrid systems". <i>Information Fusion</i>. <b>16</b>: 3–17. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.inffus.2013.04.006">10.1016/j.inffus.2013.04.006</a>. <a href="/wiki/Hdl_(identifier)" class="mw-redirect" title="Hdl (identifier)">hdl</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://hdl.handle.net/10366%2F134320">10366/134320</a></span>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:11632848">11632848</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Information+Fusion&rft.atitle=A+survey+of+multiple+classifier+systems+as+hybrid+systems&rft.volume=16&rft.pages=3-17&rft.date=2014-03&rft_id=info%3Ahdl%2F10366%2F134320&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A11632848%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1016%2Fj.inffus.2013.04.006&rft.aulast=Wo%C5%BAniak&rft.aufirst=Micha%C5%82&rft.au=Gra%C3%B1a%2C+Manuel&rft.au=Corchado%2C+Emilio&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-rodriguez-50"><span class="mw-cite-backlink">^ <a href="#cite_ref-rodriguez_50-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-rodriguez_50-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFRodriguez-GalianoGhimireRoganChica-Olmo2012" class="citation journal cs1">Rodriguez-Galiano, V.F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J.P. (January 2012). "An assessment of the effectiveness of a random forest classifier for land-cover classification". <i>ISPRS Journal of Photogrammetry and Remote Sensing</i>. <b>67</b>: 93–104. <a href="/wiki/Bibcode_(identifier)" class="mw-redirect" title="Bibcode (identifier)">Bibcode</a>:<a rel="nofollow" class="external text" href="https://ui.adsabs.harvard.edu/abs/2012JPRS...67...93R">2012JPRS...67...93R</a>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.isprsjprs.2011.11.002">10.1016/j.isprsjprs.2011.11.002</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=ISPRS+Journal+of+Photogrammetry+and+Remote+Sensing&rft.atitle=An+assessment+of+the+effectiveness+of+a+random+forest+classifier+for+land-cover+classification&rft.volume=67&rft.pages=93-104&rft.date=2012-01&rft_id=info%3Adoi%2F10.1016%2Fj.isprsjprs.2011.11.002&rft_id=info%3Abibcode%2F2012JPRS...67...93R&rft.aulast=Rodriguez-Galiano&rft.aufirst=V.F.&rft.au=Ghimire%2C+B.&rft.au=Rogan%2C+J.&rft.au=Chica-Olmo%2C+M.&rft.au=Rigol-Sanchez%2C+J.P.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-51"><span class="mw-cite-backlink"><b><a href="#cite_ref-51">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFGiacintoRoli2001" class="citation journal cs1">Giacinto, Giorgio; Roli, Fabio (August 2001). "Design of effective neural network ensembles for image classification purposes". <i>Image and Vision Computing</i>. <b>19</b> (9–10): 699–707. <a href="/wiki/CiteSeerX_(identifier)" class="mw-redirect" title="CiteSeerX (identifier)">CiteSeerX</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.5820">10.1.1.11.5820</a></span>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2FS0262-8856%2801%2900045-2">10.1016/S0262-8856(01)00045-2</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Image+and+Vision+Computing&rft.atitle=Design+of+effective+neural+network+ensembles+for+image+classification+purposes&rft.volume=19&rft.issue=9%E2%80%9310&rft.pages=699-707&rft.date=2001-08&rft_id=https%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fsummary%3Fdoi%3D10.1.1.11.5820%23id-name%3DCiteSeerX&rft_id=info%3Adoi%2F10.1016%2FS0262-8856%2801%2900045-2&rft.aulast=Giacinto&rft.aufirst=Giorgio&rft.au=Roli%2C+Fabio&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-52"><span class="mw-cite-backlink"><b><a href="#cite_ref-52">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFXiaYokoyaIwasaki2017" class="citation book cs1">Xia, Junshi; Yokoya, Naoto; Iwasaki, Yakira (March 2017). "A novel ensemble classifier of hyperspectral and LiDAR data using morphological features". <i>2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</i>. pp. 6185–6189. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1109%2FICASSP.2017.7953345">10.1109/ICASSP.2017.7953345</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-1-5090-4117-6" title="Special:BookSources/978-1-5090-4117-6"><bdi>978-1-5090-4117-6</bdi></a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:40210273">40210273</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=A+novel+ensemble+classifier+of+hyperspectral+and+LiDAR+data+using+morphological+features&rft.btitle=2017+IEEE+International+Conference+on+Acoustics%2C+Speech+and+Signal+Processing+%28ICASSP%29&rft.pages=6185-6189&rft.date=2017-03&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A40210273%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1109%2FICASSP.2017.7953345&rft.isbn=978-1-5090-4117-6&rft.aulast=Xia&rft.aufirst=Junshi&rft.au=Yokoya%2C+Naoto&rft.au=Iwasaki%2C+Yakira&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-53"><span class="mw-cite-backlink"><b><a href="#cite_ref-53">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFMochizukiMurakami2012" class="citation journal cs1">Mochizuki, S.; Murakami, T. (November 2012). "Accuracy comparison of land cover mapping using the object-oriented image classification with machine learning algorithms". <i>33rd Asian Conference on Remote Sensing 2012, ACRS 2012</i>. <b>1</b>: 126–133.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=33rd+Asian+Conference+on+Remote+Sensing+2012%2C+ACRS+2012&rft.atitle=Accuracy+comparison+of+land+cover+mapping+using+the+object-oriented+image+classification+with+machine+learning+algorithms&rft.volume=1&rft.pages=126-133&rft.date=2012-11&rft.aulast=Mochizuki&rft.aufirst=S.&rft.au=Murakami%2C+T.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-54"><span class="mw-cite-backlink"><b><a href="#cite_ref-54">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFLiuTomanFullerChen2018" class="citation journal cs1">Liu, Dan; Toman, Elizabeth; Fuller, Zane; Chen, Gang; Londo, Alexis; Xuesong, Zhang; Kaiguang, Zhao (2018). <a rel="nofollow" class="external text" href="https://pages.charlotte.edu/gang-chen/wp-content/uploads/sites/184/2018/08/Liu_2018_Intigration-historical-map-aerial-imagery-LCLUC.pdf">"Integration of historical map and aerial imagery to characterize long-term land-use change and landscape dynamics: An object-based analysis via Random Forests"</a> <span class="cs1-format">(PDF)</span>. <i>Ecological Indicators</i>. <b>95</b> (1): 595–605. <a href="/wiki/Bibcode_(identifier)" class="mw-redirect" title="Bibcode (identifier)">Bibcode</a>:<a rel="nofollow" class="external text" href="https://ui.adsabs.harvard.edu/abs/2018EcInd..95..595L">2018EcInd..95..595L</a>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.ecolind.2018.08.004">10.1016/j.ecolind.2018.08.004</a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:92025959">92025959</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Ecological+Indicators&rft.atitle=Integration+of+historical+map+and+aerial+imagery+to+characterize+long-term+land-use+change+and+landscape+dynamics%3A+An+object-based+analysis+via+Random+Forests&rft.volume=95&rft.issue=1&rft.pages=595-605&rft.date=2018&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A92025959%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1016%2Fj.ecolind.2018.08.004&rft_id=info%3Abibcode%2F2018EcInd..95..595L&rft.aulast=Liu&rft.aufirst=Dan&rft.au=Toman%2C+Elizabeth&rft.au=Fuller%2C+Zane&rft.au=Chen%2C+Gang&rft.au=Londo%2C+Alexis&rft.au=Xuesong%2C+Zhang&rft.au=Kaiguang%2C+Zhao&rft_id=https%3A%2F%2Fpages.charlotte.edu%2Fgang-chen%2Fwp-content%2Fuploads%2Fsites%2F184%2F2018%2F08%2FLiu_2018_Intigration-historical-map-aerial-imagery-LCLUC.pdf&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-55"><span class="mw-cite-backlink"><b><a href="#cite_ref-55">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFGiacintoRoliFumera2000" class="citation book cs1">Giacinto, G.; Roli, F.; Fumera, G. (September 2000). "Design of effective multiple classifier systems by clustering of classifiers". <i>Proceedings 15th International Conference on Pattern Recognition. ICPR-2000</i>. Vol. 2. pp. 160–163. <a href="/wiki/CiteSeerX_(identifier)" class="mw-redirect" title="CiteSeerX (identifier)">CiteSeerX</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.5328">10.1.1.11.5328</a></span>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1109%2FICPR.2000.906039">10.1109/ICPR.2000.906039</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-7695-0750-7" title="Special:BookSources/978-0-7695-0750-7"><bdi>978-0-7695-0750-7</bdi></a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:2625643">2625643</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=Design+of+effective+multiple+classifier+systems+by+clustering+of+classifiers&rft.btitle=Proceedings+15th+International+Conference+on+Pattern+Recognition.+ICPR-2000&rft.pages=160-163&rft.date=2000-09&rft_id=https%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fsummary%3Fdoi%3D10.1.1.11.5328%23id-name%3DCiteSeerX&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A2625643%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1109%2FICPR.2000.906039&rft.isbn=978-0-7695-0750-7&rft.aulast=Giacinto&rft.aufirst=G.&rft.au=Roli%2C+F.&rft.au=Fumera%2C+G.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-s2-56"><span class="mw-cite-backlink"><b><a href="#cite_ref-s2_56-0">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFDuLiuXiaZhao2013" class="citation journal cs1">Du, Peijun; Liu, Sicong; Xia, Junshi; Zhao, Yindi (January 2013). "Information fusion techniques for change detection from multi-temporal remote sensing images". <i>Information Fusion</i>. <b>14</b> (1): 19–27. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.inffus.2012.05.003">10.1016/j.inffus.2012.05.003</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Information+Fusion&rft.atitle=Information+fusion+techniques+for+change+detection+from+multi-temporal+remote+sensing+images&rft.volume=14&rft.issue=1&rft.pages=19-27&rft.date=2013-01&rft_id=info%3Adoi%2F10.1016%2Fj.inffus.2012.05.003&rft.aulast=Du&rft.aufirst=Peijun&rft.au=Liu%2C+Sicong&rft.au=Xia%2C+Junshi&rft.au=Zhao%2C+Yindi&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-57"><span class="mw-cite-backlink"><b><a href="#cite_ref-57">^</a></b></span> <span class="reference-text">Defined by Bruzzone et al. (2002) as "The data class that receives the largest number of votes is taken as the class of the input pattern", this is <i>simple majority</i>, more accurately described as <i>plurality</i> voting.</span> </li> <li id="cite_note-58"><span class="mw-cite-backlink"><b><a href="#cite_ref-58">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFZhaoWulderHuBright2019" class="citation journal cs1">Zhao, Kaiguang; Wulder, Michael A; Hu, Tongx; Bright, Ryan; Wu, Qiusheng; Qin, Haiming; Li, Yang (2019). <a rel="nofollow" class="external text" href="https://go.osu.edu/beast2019">"Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm"</a>. <i>Remote Sensing of Environment</i>. <b>232</b>: 111181. <a href="/wiki/Bibcode_(identifier)" class="mw-redirect" title="Bibcode (identifier)">Bibcode</a>:<a rel="nofollow" class="external text" href="https://ui.adsabs.harvard.edu/abs/2019RSEnv.23211181Z">2019RSEnv.23211181Z</a>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.rse.2019.04.034">10.1016/j.rse.2019.04.034</a></span>. <a href="/wiki/Hdl_(identifier)" class="mw-redirect" title="Hdl (identifier)">hdl</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://hdl.handle.net/11250%2F2651134">11250/2651134</a></span>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:201310998">201310998</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Remote+Sensing+of+Environment&rft.atitle=Detecting+change-point%2C+trend%2C+and+seasonality+in+satellite+time+series+data+to+track+abrupt+changes+and+nonlinear+dynamics%3A+A+Bayesian+ensemble+algorithm&rft.volume=232&rft.pages=111181&rft.date=2019&rft_id=info%3Ahdl%2F11250%2F2651134&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A201310998%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1016%2Fj.rse.2019.04.034&rft_id=info%3Abibcode%2F2019RSEnv.23211181Z&rft.aulast=Zhao&rft.aufirst=Kaiguang&rft.au=Wulder%2C+Michael+A&rft.au=Hu%2C+Tongx&rft.au=Bright%2C+Ryan&rft.au=Wu%2C+Qiusheng&rft.au=Qin%2C+Haiming&rft.au=Li%2C+Yang&rft_id=https%3A%2F%2Fgo.osu.edu%2Fbeast2019&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-Bruzzone_et_al_2002-59"><span class="mw-cite-backlink"><b><a href="#cite_ref-Bruzzone_et_al_2002_59-0">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFBruzzoneCossuVernazza2002" class="citation journal cs1">Bruzzone, Lorenzo; Cossu, Roberto; Vernazza, Gianni (December 2002). <a rel="nofollow" class="external text" href="http://eprints.biblio.unitn.it/105/1/24.pdf">"Combining parametric and non-parametric algorithms for a partially unsupervised classification of multitemporal remote-sensing images"</a> <span class="cs1-format">(PDF)</span>. <i>Information Fusion</i>. <b>3</b> (4): 289–297. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2FS1566-2535%2802%2900091-X">10.1016/S1566-2535(02)00091-X</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Information+Fusion&rft.atitle=Combining+parametric+and+non-parametric+algorithms+for+a+partially+unsupervised+classification+of+multitemporal+remote-sensing+images&rft.volume=3&rft.issue=4&rft.pages=289-297&rft.date=2002-12&rft_id=info%3Adoi%2F10.1016%2FS1566-2535%2802%2900091-X&rft.aulast=Bruzzone&rft.aufirst=Lorenzo&rft.au=Cossu%2C+Roberto&rft.au=Vernazza%2C+Gianni&rft_id=http%3A%2F%2Feprints.biblio.unitn.it%2F105%2F1%2F24.pdf&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-60"><span class="mw-cite-backlink"><b><a href="#cite_ref-60">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFTheodomirNascettiBan.2020" class="citation journal cs1">Theodomir, Mugiraneza; Nascetti, Andrea; Ban., Yifang (2020). <a rel="nofollow" class="external text" href="https://doi.org/10.3390%2Frs12182883">"Continuous monitoring of urban land cover change trajectories with landsat time series and landtrendr-google earth engine cloud computing"</a>. <i>Remote Sensing</i>. <b>12</b> (18): 2883. <a href="/wiki/Bibcode_(identifier)" class="mw-redirect" title="Bibcode (identifier)">Bibcode</a>:<a rel="nofollow" class="external text" href="https://ui.adsabs.harvard.edu/abs/2020RemS...12.2883M">2020RemS...12.2883M</a>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.3390%2Frs12182883">10.3390/rs12182883</a></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Remote+Sensing&rft.atitle=Continuous+monitoring+of+urban+land+cover+change+trajectories+with+landsat+time+series+and+landtrendr-google+earth+engine+cloud+computing&rft.volume=12&rft.issue=18&rft.pages=2883&rft.date=2020&rft_id=info%3Adoi%2F10.3390%2Frs12182883&rft_id=info%3Abibcode%2F2020RemS...12.2883M&rft.aulast=Theodomir&rft.aufirst=Mugiraneza&rft.au=Nascetti%2C+Andrea&rft.au=Ban.%2C+Yifang&rft_id=https%3A%2F%2Fdoi.org%2F10.3390%252Frs12182883&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-61"><span class="mw-cite-backlink"><b><a href="#cite_ref-61">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFLiZhaoHuZhang" class="citation web cs1">Li, Yang; Zhao, Kaiguang; Hu, Tongxi; Zhang, Xuesong. <a rel="nofollow" class="external text" href="https://github.com/zhaokg/Rbeast">"BEAST: A Bayesian Ensemble Algorithm for Change-Point Detection and Time Series Decomposition"</a>. <i><a href="/wiki/GitHub" title="GitHub">GitHub</a></i>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=GitHub&rft.atitle=BEAST%3A+A+Bayesian+Ensemble+Algorithm+for+Change-Point+Detection+and+Time+Series+Decomposition&rft.aulast=Li&rft.aufirst=Yang&rft.au=Zhao%2C+Kaiguang&rft.au=Hu%2C+Tongxi&rft.au=Zhang%2C+Xuesong&rft_id=https%3A%2F%2Fgithub.com%2Fzhaokg%2FRbeast&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-62"><span class="mw-cite-backlink"><b><a href="#cite_ref-62">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFRaj_KumarSelvakumar2011" class="citation journal cs1">Raj Kumar, P. Arun; Selvakumar, S. (July 2011). "Distributed denial of service attack detection using an ensemble of neural classifier". <i>Computer Communications</i>. <b>34</b> (11): 1328–1341. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.comcom.2011.01.012">10.1016/j.comcom.2011.01.012</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Computer+Communications&rft.atitle=Distributed+denial+of+service+attack+detection+using+an+ensemble+of+neural+classifier&rft.volume=34&rft.issue=11&rft.pages=1328-1341&rft.date=2011-07&rft_id=info%3Adoi%2F10.1016%2Fj.comcom.2011.01.012&rft.aulast=Raj+Kumar&rft.aufirst=P.+Arun&rft.au=Selvakumar%2C+S.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-63"><span class="mw-cite-backlink"><b><a href="#cite_ref-63">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFShabtaiMoskovitchEloviciGlezer2009" class="citation journal cs1">Shabtai, Asaf; Moskovitch, Robert; Elovici, Yuval; Glezer, Chanan (February 2009). "Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey". <i>Information Security Technical Report</i>. <b>14</b> (1): 16–29. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.istr.2009.03.003">10.1016/j.istr.2009.03.003</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Information+Security+Technical+Report&rft.atitle=Detection+of+malicious+code+by+applying+machine+learning+classifiers+on+static+features%3A+A+state-of-the-art+survey&rft.volume=14&rft.issue=1&rft.pages=16-29&rft.date=2009-02&rft_id=info%3Adoi%2F10.1016%2Fj.istr.2009.03.003&rft.aulast=Shabtai&rft.aufirst=Asaf&rft.au=Moskovitch%2C+Robert&rft.au=Elovici%2C+Yuval&rft.au=Glezer%2C+Chanan&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-64"><span class="mw-cite-backlink"><b><a href="#cite_ref-64">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFZhangYinHaoZhang2007" class="citation book cs1">Zhang, Boyun; Yin, Jianping; Hao, Jingbo; Zhang, Dingxing; Wang, Shulin (2007). "Malicious Codes Detection Based on Ensemble Learning". <i>Autonomic and Trusted Computing</i>. Lecture Notes in Computer Science. Vol. 4610. pp. 468–477. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1007%2F978-3-540-73547-2_48">10.1007/978-3-540-73547-2_48</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-3-540-73546-5" title="Special:BookSources/978-3-540-73546-5"><bdi>978-3-540-73546-5</bdi></a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=Malicious+Codes+Detection+Based+on+Ensemble+Learning&rft.btitle=Autonomic+and+Trusted+Computing&rft.series=Lecture+Notes+in+Computer+Science&rft.pages=468-477&rft.date=2007&rft_id=info%3Adoi%2F10.1007%2F978-3-540-73547-2_48&rft.isbn=978-3-540-73546-5&rft.aulast=Zhang&rft.aufirst=Boyun&rft.au=Yin%2C+Jianping&rft.au=Hao%2C+Jingbo&rft.au=Zhang%2C+Dingxing&rft.au=Wang%2C+Shulin&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-65"><span class="mw-cite-backlink"><b><a href="#cite_ref-65">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFMenahemShabtaiRokachElovici2009" class="citation journal cs1">Menahem, Eitan; Shabtai, Asaf; Rokach, Lior; Elovici, Yuval (February 2009). "Improving malware detection by applying multi-inducer ensemble". <i>Computational Statistics & Data Analysis</i>. <b>53</b> (4): 1483–1494. <a href="/wiki/CiteSeerX_(identifier)" class="mw-redirect" title="CiteSeerX (identifier)">CiteSeerX</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.2722">10.1.1.150.2722</a></span>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.csda.2008.10.015">10.1016/j.csda.2008.10.015</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Computational+Statistics+%26+Data+Analysis&rft.atitle=Improving+malware+detection+by+applying+multi-inducer+ensemble&rft.volume=53&rft.issue=4&rft.pages=1483-1494&rft.date=2009-02&rft_id=https%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fsummary%3Fdoi%3D10.1.1.150.2722%23id-name%3DCiteSeerX&rft_id=info%3Adoi%2F10.1016%2Fj.csda.2008.10.015&rft.aulast=Menahem&rft.aufirst=Eitan&rft.au=Shabtai%2C+Asaf&rft.au=Rokach%2C+Lior&rft.au=Elovici%2C+Yuval&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-66"><span class="mw-cite-backlink"><b><a href="#cite_ref-66">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFLocastoWangKeromytisSalvatore2005" class="citation book cs1">Locasto, Michael E.; Wang, Ke; Keromytis, Angeles D.; Salvatore, J. Stolfo (2005). "FLIPS: Hybrid Adaptive Intrusion Prevention". <i>Recent Advances in Intrusion Detection</i>. Lecture Notes in Computer Science. Vol. 3858. pp. 82–101. <a href="/wiki/CiteSeerX_(identifier)" class="mw-redirect" title="CiteSeerX (identifier)">CiteSeerX</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.3798">10.1.1.60.3798</a></span>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1007%2F11663812_5">10.1007/11663812_5</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-3-540-31778-4" title="Special:BookSources/978-3-540-31778-4"><bdi>978-3-540-31778-4</bdi></a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=FLIPS%3A+Hybrid+Adaptive+Intrusion+Prevention&rft.btitle=Recent+Advances+in+Intrusion+Detection&rft.series=Lecture+Notes+in+Computer+Science&rft.pages=82-101&rft.date=2005&rft_id=https%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fsummary%3Fdoi%3D10.1.1.60.3798%23id-name%3DCiteSeerX&rft_id=info%3Adoi%2F10.1007%2F11663812_5&rft.isbn=978-3-540-31778-4&rft.aulast=Locasto&rft.aufirst=Michael+E.&rft.au=Wang%2C+Ke&rft.au=Keromytis%2C+Angeles+D.&rft.au=Salvatore%2C+J.+Stolfo&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-67"><span class="mw-cite-backlink"><b><a href="#cite_ref-67">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFGiacintoPerdisciDel_RioRoli2008" class="citation journal cs1">Giacinto, Giorgio; Perdisci, Roberto; Del Rio, Mauro; Roli, Fabio (January 2008). "Intrusion detection in computer networks by a modular ensemble of one-class classifiers". <i>Information Fusion</i>. <b>9</b> (1): 69–82. <a href="/wiki/CiteSeerX_(identifier)" class="mw-redirect" title="CiteSeerX (identifier)">CiteSeerX</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.69.9132">10.1.1.69.9132</a></span>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.inffus.2006.10.002">10.1016/j.inffus.2006.10.002</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Information+Fusion&rft.atitle=Intrusion+detection+in+computer+networks+by+a+modular+ensemble+of+one-class+classifiers&rft.volume=9&rft.issue=1&rft.pages=69-82&rft.date=2008-01&rft_id=https%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fsummary%3Fdoi%3D10.1.1.69.9132%23id-name%3DCiteSeerX&rft_id=info%3Adoi%2F10.1016%2Fj.inffus.2006.10.002&rft.aulast=Giacinto&rft.aufirst=Giorgio&rft.au=Perdisci%2C+Roberto&rft.au=Del+Rio%2C+Mauro&rft.au=Roli%2C+Fabio&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-68"><span class="mw-cite-backlink"><b><a href="#cite_ref-68">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFMuLuWattaHassoun2009" class="citation book cs1">Mu, Xiaoyan; Lu, Jiangfeng; Watta, Paul; Hassoun, Mohamad H. (July 2009). "Weighted voting-based ensemble classifiers with application to human face recognition and voice recognition". <i>2009 International Joint Conference on Neural Networks</i>. pp. 2168–2171. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1109%2FIJCNN.2009.5178708">10.1109/IJCNN.2009.5178708</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-1-4244-3548-7" title="Special:BookSources/978-1-4244-3548-7"><bdi>978-1-4244-3548-7</bdi></a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:18850747">18850747</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=Weighted+voting-based+ensemble+classifiers+with+application+to+human+face+recognition+and+voice+recognition&rft.btitle=2009+International+Joint+Conference+on+Neural+Networks&rft.pages=2168-2171&rft.date=2009-07&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A18850747%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1109%2FIJCNN.2009.5178708&rft.isbn=978-1-4244-3548-7&rft.aulast=Mu&rft.aufirst=Xiaoyan&rft.au=Lu%2C+Jiangfeng&rft.au=Watta%2C+Paul&rft.au=Hassoun%2C+Mohamad+H.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-69"><span class="mw-cite-backlink"><b><a href="#cite_ref-69">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFYuShanChenGao2006" class="citation book cs1">Yu, Su; Shan, Shiguang; Chen, Xilin; Gao, Wen (April 2006). "Hierarchical ensemble of Gabor Fisher classifier for face recognition". <i>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</i>. pp. 91–96. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1109%2FFGR.2006.64">10.1109/FGR.2006.64</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-7695-2503-7" title="Special:BookSources/978-0-7695-2503-7"><bdi>978-0-7695-2503-7</bdi></a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:1513315">1513315</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=Hierarchical+ensemble+of+Gabor+Fisher+classifier+for+face+recognition&rft.btitle=7th+International+Conference+on+Automatic+Face+and+Gesture+Recognition+%28FGR06%29&rft.pages=91-96&rft.date=2006-04&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A1513315%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1109%2FFGR.2006.64&rft.isbn=978-0-7695-2503-7&rft.aulast=Yu&rft.aufirst=Su&rft.au=Shan%2C+Shiguang&rft.au=Chen%2C+Xilin&rft.au=Gao%2C+Wen&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-70"><span class="mw-cite-backlink"><b><a href="#cite_ref-70">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFSuShanChenGao2006" class="citation book cs1">Su, Y.; Shan, S.; Chen, X.; Gao, W. (September 2006). "Patch-Based Gabor Fisher Classifier for Face Recognition". <i>18th International Conference on Pattern Recognition (ICPR'06)</i>. Vol. 2. pp. 528–531. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1109%2FICPR.2006.917">10.1109/ICPR.2006.917</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-7695-2521-1" title="Special:BookSources/978-0-7695-2521-1"><bdi>978-0-7695-2521-1</bdi></a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:5381806">5381806</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=Patch-Based+Gabor+Fisher+Classifier+for+Face+Recognition&rft.btitle=18th+International+Conference+on+Pattern+Recognition+%28ICPR%2706%29&rft.pages=528-531&rft.date=2006-09&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A5381806%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1109%2FICPR.2006.917&rft.isbn=978-0-7695-2521-1&rft.aulast=Su&rft.aufirst=Y.&rft.au=Shan%2C+S.&rft.au=Chen%2C+X.&rft.au=Gao%2C+W.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-71"><span class="mw-cite-backlink"><b><a href="#cite_ref-71">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFLiuLinChen2008" class="citation book cs1">Liu, Yang; Lin, Yongzheng; Chen, Yuehui (July 2008). "Ensemble Classification Based on ICA for Face Recognition". <i>2008 Congress on Image and Signal Processing</i>. pp. 144–148. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1109%2FCISP.2008.581">10.1109/CISP.2008.581</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-7695-3119-9" title="Special:BookSources/978-0-7695-3119-9"><bdi>978-0-7695-3119-9</bdi></a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:16248842">16248842</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=Ensemble+Classification+Based+on+ICA+for+Face+Recognition&rft.btitle=2008+Congress+on+Image+and+Signal+Processing&rft.pages=144-148&rft.date=2008-07&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A16248842%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1109%2FCISP.2008.581&rft.isbn=978-0-7695-3119-9&rft.aulast=Liu&rft.aufirst=Yang&rft.au=Lin%2C+Yongzheng&rft.au=Chen%2C+Yuehui&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-72"><span class="mw-cite-backlink"><b><a href="#cite_ref-72">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFRiegerMuraleedharanRamachandran2014" class="citation book cs1">Rieger, Steven A.; Muraleedharan, Rajani; Ramachandran, Ravi P. (2014). "Speech based emotion recognition using spectral feature extraction and an ensemble of KNN classifiers". <i>The 9th International Symposium on Chinese Spoken Language Processing</i>. pp. 589–593. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1109%2FISCSLP.2014.6936711">10.1109/ISCSLP.2014.6936711</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-1-4799-4219-0" title="Special:BookSources/978-1-4799-4219-0"><bdi>978-1-4799-4219-0</bdi></a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:31370450">31370450</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=Speech+based+emotion+recognition+using+spectral+feature+extraction+and+an+ensemble+of+KNN+classifiers&rft.btitle=The+9th+International+Symposium+on+Chinese+Spoken+Language+Processing&rft.pages=589-593&rft.date=2014&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A31370450%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1109%2FISCSLP.2014.6936711&rft.isbn=978-1-4799-4219-0&rft.aulast=Rieger&rft.aufirst=Steven+A.&rft.au=Muraleedharan%2C+Rajani&rft.au=Ramachandran%2C+Ravi+P.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-73"><span class="mw-cite-backlink"><b><a href="#cite_ref-73">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFKrajewskiBatlinerKessel2010" class="citation book cs1">Krajewski, Jarek; Batliner, Anton; Kessel, Silke (October 2010). "Comparing Multiple Classifiers for Speech-Based Detection of Self-Confidence - A Pilot Study". <a rel="nofollow" class="external text" href="https://opus.bibliothek.uni-augsburg.de/opus4/files/69603/69603.pdf"><i>2010 20th International Conference on Pattern Recognition</i></a> <span class="cs1-format">(PDF)</span>. pp. 3716–3719. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1109%2FICPR.2010.905">10.1109/ICPR.2010.905</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-1-4244-7542-1" title="Special:BookSources/978-1-4244-7542-1"><bdi>978-1-4244-7542-1</bdi></a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:15431610">15431610</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=Comparing+Multiple+Classifiers+for+Speech-Based+Detection+of+Self-Confidence+-+A+Pilot+Study&rft.btitle=2010+20th+International+Conference+on+Pattern+Recognition&rft.pages=3716-3719&rft.date=2010-10&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A15431610%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1109%2FICPR.2010.905&rft.isbn=978-1-4244-7542-1&rft.aulast=Krajewski&rft.aufirst=Jarek&rft.au=Batliner%2C+Anton&rft.au=Kessel%2C+Silke&rft_id=https%3A%2F%2Fopus.bibliothek.uni-augsburg.de%2Fopus4%2Ffiles%2F69603%2F69603.pdf&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-74"><span class="mw-cite-backlink"><b><a href="#cite_ref-74">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFRaniMuneeswaran2016" class="citation journal cs1">Rani, P. Ithaya; Muneeswaran, K. (25 May 2016). "Recognize the facial emotion in video sequences using eye and mouth temporal Gabor features". <i>Multimedia Tools and Applications</i>. <b>76</b> (7): 10017–10040. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1007%2Fs11042-016-3592-y">10.1007/s11042-016-3592-y</a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:20143585">20143585</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Multimedia+Tools+and+Applications&rft.atitle=Recognize+the+facial+emotion+in+video+sequences+using+eye+and+mouth+temporal+Gabor+features&rft.volume=76&rft.issue=7&rft.pages=10017-10040&rft.date=2016-05-25&rft_id=info%3Adoi%2F10.1007%2Fs11042-016-3592-y&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A20143585%23id-name%3DS2CID&rft.aulast=Rani&rft.aufirst=P.+Ithaya&rft.au=Muneeswaran%2C+K.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-75"><span class="mw-cite-backlink"><b><a href="#cite_ref-75">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFRaniMuneeswaran2016" class="citation journal cs1">Rani, P. Ithaya; Muneeswaran, K. (August 2016). "Facial Emotion Recognition Based on Eye and Mouth Regions". <i>International Journal of Pattern Recognition and Artificial Intelligence</i>. <b>30</b> (7): 1655020. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1142%2FS021800141655020X">10.1142/S021800141655020X</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=International+Journal+of+Pattern+Recognition+and+Artificial+Intelligence&rft.atitle=Facial+Emotion+Recognition+Based+on+Eye+and+Mouth+Regions&rft.volume=30&rft.issue=7&rft.pages=1655020&rft.date=2016-08&rft_id=info%3Adoi%2F10.1142%2FS021800141655020X&rft.aulast=Rani&rft.aufirst=P.+Ithaya&rft.au=Muneeswaran%2C+K.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-76"><span class="mw-cite-backlink"><b><a href="#cite_ref-76">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFRaniMuneeswaran2018" class="citation journal cs1">Rani, P. Ithaya; Muneeswaran, K (28 March 2018). <a rel="nofollow" class="external text" href="https://doi.org/10.1007%2Fs12046-018-0801-6">"Emotion recognition based on facial components"</a>. <i>Sādhanā</i>. <b>43</b> (3). <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1007%2Fs12046-018-0801-6">10.1007/s12046-018-0801-6</a></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=S%C4%81dhan%C4%81&rft.atitle=Emotion+recognition+based+on+facial+components&rft.volume=43&rft.issue=3&rft.date=2018-03-28&rft_id=info%3Adoi%2F10.1007%2Fs12046-018-0801-6&rft.aulast=Rani&rft.aufirst=P.+Ithaya&rft.au=Muneeswaran%2C+K&rft_id=https%3A%2F%2Fdoi.org%2F10.1007%252Fs12046-018-0801-6&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-77"><span class="mw-cite-backlink"><b><a href="#cite_ref-77">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFLouzadaAra2012" class="citation journal cs1">Louzada, Francisco; Ara, Anderson (October 2012). "Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool". <i>Expert Systems with Applications</i>. <b>39</b> (14): 11583–11592. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.eswa.2012.04.024">10.1016/j.eswa.2012.04.024</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Expert+Systems+with+Applications&rft.atitle=Bagging+k-dependence+probabilistic+networks%3A+An+alternative+powerful+fraud+detection+tool&rft.volume=39&rft.issue=14&rft.pages=11583-11592&rft.date=2012-10&rft_id=info%3Adoi%2F10.1016%2Fj.eswa.2012.04.024&rft.aulast=Louzada&rft.aufirst=Francisco&rft.au=Ara%2C+Anderson&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-78"><span class="mw-cite-backlink"><b><a href="#cite_ref-78">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFSundarkumarRavi2015" class="citation journal cs1">Sundarkumar, G. Ganesh; Ravi, Vadlamani (January 2015). "A novel hybrid undersampling method for mining unbalanced datasets in banking and insurance". <i>Engineering Applications of Artificial Intelligence</i>. <b>37</b>: 368–377. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.engappai.2014.09.019">10.1016/j.engappai.2014.09.019</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Engineering+Applications+of+Artificial+Intelligence&rft.atitle=A+novel+hybrid+undersampling+method+for+mining+unbalanced+datasets+in+banking+and+insurance&rft.volume=37&rft.pages=368-377&rft.date=2015-01&rft_id=info%3Adoi%2F10.1016%2Fj.engappai.2014.09.019&rft.aulast=Sundarkumar&rft.aufirst=G.+Ganesh&rft.au=Ravi%2C+Vadlamani&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-ReferenceA-79"><span class="mw-cite-backlink">^ <a href="#cite_ref-ReferenceA_79-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-ReferenceA_79-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFKimSohn2012" class="citation journal cs1">Kim, Yoonseong; Sohn, So Young (August 2012). "Stock fraud detection using peer group analysis". <i>Expert Systems with Applications</i>. <b>39</b> (10): 8986–8992. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.eswa.2012.02.025">10.1016/j.eswa.2012.02.025</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Expert+Systems+with+Applications&rft.atitle=Stock+fraud+detection+using+peer+group+analysis&rft.volume=39&rft.issue=10&rft.pages=8986-8992&rft.date=2012-08&rft_id=info%3Adoi%2F10.1016%2Fj.eswa.2012.02.025&rft.aulast=Kim&rft.aufirst=Yoonseong&rft.au=Sohn%2C+So+Young&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-80"><span class="mw-cite-backlink"><b><a href="#cite_ref-80">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFSavioGarcía-SebastiánChyzykHernandez2011" class="citation journal cs1">Savio, A.; García-Sebastián, M.T.; Chyzyk, D.; Hernandez, C.; Graña, M.; Sistiaga, A.; López de Munain, A.; Villanúa, J. (August 2011). "Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI". <i>Computers in Biology and Medicine</i>. <b>41</b> (8): 600–610. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.compbiomed.2011.05.010">10.1016/j.compbiomed.2011.05.010</a>. <a href="/wiki/PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/21621760">21621760</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Computers+in+Biology+and+Medicine&rft.atitle=Neurocognitive+disorder+detection+based+on+feature+vectors+extracted+from+VBM+analysis+of+structural+MRI&rft.volume=41&rft.issue=8&rft.pages=600-610&rft.date=2011-08&rft_id=info%3Adoi%2F10.1016%2Fj.compbiomed.2011.05.010&rft_id=info%3Apmid%2F21621760&rft.aulast=Savio&rft.aufirst=A.&rft.au=Garc%C3%ADa-Sebasti%C3%A1n%2C+M.T.&rft.au=Chyzyk%2C+D.&rft.au=Hernandez%2C+C.&rft.au=Gra%C3%B1a%2C+M.&rft.au=Sistiaga%2C+A.&rft.au=L%C3%B3pez+de+Munain%2C+A.&rft.au=Villan%C3%BAa%2C+J.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-81"><span class="mw-cite-backlink"><b><a href="#cite_ref-81">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFAyerdiSavioGraña2013" class="citation book cs1">Ayerdi, B.; Savio, A.; Graña, M. (June 2013). "Meta-ensembles of Classifiers for Alzheimer's Disease Detection Using Independent ROI Features". <i>Natural and Artificial Computation in Engineering and Medical Applications</i>. Lecture Notes in Computer Science. Vol. 7931. pp. 122–130. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1007%2F978-3-642-38622-0_13">10.1007/978-3-642-38622-0_13</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-3-642-38621-3" title="Special:BookSources/978-3-642-38621-3"><bdi>978-3-642-38621-3</bdi></a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=Meta-ensembles+of+Classifiers+for+Alzheimer%27s+Disease+Detection+Using+Independent+ROI+Features&rft.btitle=Natural+and+Artificial+Computation+in+Engineering+and+Medical+Applications&rft.series=Lecture+Notes+in+Computer+Science&rft.pages=122-130&rft.date=2013-06&rft_id=info%3Adoi%2F10.1007%2F978-3-642-38622-0_13&rft.isbn=978-3-642-38621-3&rft.aulast=Ayerdi&rft.aufirst=B.&rft.au=Savio%2C+A.&rft.au=Gra%C3%B1a%2C+M.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-82"><span class="mw-cite-backlink"><b><a href="#cite_ref-82">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFGuDingZhang2015" class="citation journal cs1">Gu, Quan; Ding, Yong-Sheng; Zhang, Tong-Liang (April 2015). "An ensemble classifier based prediction of G-protein-coupled receptor classes in low homology". <i>Neurocomputing</i>. <b>154</b>: 110–118. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.neucom.2014.12.013">10.1016/j.neucom.2014.12.013</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Neurocomputing&rft.atitle=An+ensemble+classifier+based+prediction+of+G-protein-coupled+receptor+classes+in+low+homology&rft.volume=154&rft.pages=110-118&rft.date=2015-04&rft_id=info%3Adoi%2F10.1016%2Fj.neucom.2014.12.013&rft.aulast=Gu&rft.aufirst=Quan&rft.au=Ding%2C+Yong-Sheng&rft.au=Zhang%2C+Tong-Liang&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-83"><span class="mw-cite-backlink"><b><a href="#cite_ref-83">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFXueZhouLiYao2020" class="citation journal cs1">Xue, Dan; Zhou, Xiaomin; Li, Chen; Yao, Yudong; Rahaman, Md Mamunur; Zhang, Jinghua; Chen, Hao; Zhang, Jinpeng; Qi, Shouliang; Sun, Hongzan (2020). <a rel="nofollow" class="external text" href="https://doi.org/10.1109%2FACCESS.2020.2999816">"An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification"</a>. <i>IEEE Access</i>. <b>8</b>: 104603–104618. <a href="/wiki/Bibcode_(identifier)" class="mw-redirect" title="Bibcode (identifier)">Bibcode</a>:<a rel="nofollow" class="external text" href="https://ui.adsabs.harvard.edu/abs/2020IEEEA...8j4603X">2020IEEEA...8j4603X</a>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1109%2FACCESS.2020.2999816">10.1109/ACCESS.2020.2999816</a></span>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/2169-3536">2169-3536</a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:219689893">219689893</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=IEEE+Access&rft.atitle=An+Application+of+Transfer+Learning+and+Ensemble+Learning+Techniques+for+Cervical+Histopathology+Image+Classification&rft.volume=8&rft.pages=104603-104618&rft.date=2020&rft_id=info%3Adoi%2F10.1109%2FACCESS.2020.2999816&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A219689893%23id-name%3DS2CID&rft.issn=2169-3536&rft_id=info%3Abibcode%2F2020IEEEA...8j4603X&rft.aulast=Xue&rft.aufirst=Dan&rft.au=Zhou%2C+Xiaomin&rft.au=Li%2C+Chen&rft.au=Yao%2C+Yudong&rft.au=Rahaman%2C+Md+Mamunur&rft.au=Zhang%2C+Jinghua&rft.au=Chen%2C+Hao&rft.au=Zhang%2C+Jinpeng&rft.au=Qi%2C+Shouliang&rft.au=Sun%2C+Hongzan&rft_id=https%3A%2F%2Fdoi.org%2F10.1109%252FACCESS.2020.2999816&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> <li id="cite_note-84"><span class="mw-cite-backlink"><b><a href="#cite_ref-84">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFMannaKunduKaplunSinitca2021" class="citation journal cs1">Manna, Ankur; Kundu, Rohit; Kaplun, Dmitrii; Sinitca, Aleksandr; Sarkar, Ram (December 2021). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8282795">"A fuzzy rank-based ensemble of CNN models for classification of cervical cytology"</a>. <i>Scientific Reports</i>. <b>11</b> (1): 14538. <a href="/wiki/Bibcode_(identifier)" class="mw-redirect" title="Bibcode (identifier)">Bibcode</a>:<a rel="nofollow" class="external text" href="https://ui.adsabs.harvard.edu/abs/2021NatSR..1114538M">2021NatSR..1114538M</a>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1038%2Fs41598-021-93783-8">10.1038/s41598-021-93783-8</a>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/2045-2322">2045-2322</a>. <a href="/wiki/PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8282795">8282795</a></span>. <a href="/wiki/PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/34267261">34267261</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Scientific+Reports&rft.atitle=A+fuzzy+rank-based+ensemble+of+CNN+models+for+classification+of+cervical+cytology&rft.volume=11&rft.issue=1&rft.pages=14538&rft.date=2021-12&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC8282795%23id-name%3DPMC&rft_id=info%3Abibcode%2F2021NatSR..1114538M&rft_id=info%3Apmid%2F34267261&rft_id=info%3Adoi%2F10.1038%2Fs41598-021-93783-8&rft.issn=2045-2322&rft.aulast=Manna&rft.aufirst=Ankur&rft.au=Kundu%2C+Rohit&rft.au=Kaplun%2C+Dmitrii&rft.au=Sinitca%2C+Aleksandr&rft.au=Sarkar%2C+Ram&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC8282795&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></span> </li> </ol></div> <div class="mw-heading mw-heading2"><h2 id="Further_reading">Further reading</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=31" title="Edit section: Further reading"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFZhou_Zhihua2012" class="citation book cs1"><a href="/wiki/Zhou_Zhihua" class="mw-redirect" title="Zhou Zhihua">Zhou Zhihua</a> (2012). <i>Ensemble Methods: Foundations and Algorithms</i>. Chapman and Hall/CRC. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-1-439-83003-1" title="Special:BookSources/978-1-439-83003-1"><bdi>978-1-439-83003-1</bdi></a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=Ensemble+Methods%3A+Foundations+and+Algorithms&rft.pub=Chapman+and+Hall%2FCRC&rft.date=2012&rft.isbn=978-1-439-83003-1&rft.au=Zhou+Zhihua&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></li> <li><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFRobert_SchapireYoav_Freund2012" class="citation book cs1"><a href="/wiki/Robert_Schapire" title="Robert Schapire">Robert Schapire</a>; <a href="/wiki/Yoav_Freund" title="Yoav Freund">Yoav Freund</a> (2012). <i>Boosting: Foundations and Algorithms</i>. MIT. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-262-01718-3" title="Special:BookSources/978-0-262-01718-3"><bdi>978-0-262-01718-3</bdi></a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=Boosting%3A+Foundations+and+Algorithms&rft.pub=MIT&rft.date=2012&rft.isbn=978-0-262-01718-3&rft.au=Robert+Schapire&rft.au=Yoav+Freund&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></li></ul> <div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Ensemble_learning&action=edit&section=32" title="Edit section: External links"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFRobi_Polikar" class="citation web cs1">Robi Polikar (ed.). <a rel="nofollow" class="external text" href="http://www.scholarpedia.org/article/Ensemble_learning">"Ensemble learning"</a>. <i><a href="/wiki/Scholarpedia" title="Scholarpedia">Scholarpedia</a></i>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=Scholarpedia&rft.atitle=Ensemble+learning&rft_id=http%3A%2F%2Fwww.scholarpedia.org%2Farticle%2FEnsemble_learning&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEnsemble+learning" class="Z3988"></span></li> <li>The <a href="/wiki/Waffles_(machine_learning)" title="Waffles (machine learning)">Waffles (machine learning)</a> toolkit contains implementations of Bagging, Boosting, Bayesian Model Averaging, Bayesian Model Combination, Bucket-of-models, and other ensemble techniques</li></ul>    </div><noscript><img src="https://login.wikimedia.org/wiki/Special:CentralAutoLogin/start?type=1x1" alt="" width="1" height="1" style="border: none; position: absolute;"></noscript> <div class="printfooter" data-nosnippet="">Retrieved from "<a dir="ltr" href="https://en.wikipedia.org/w/index.php?title=Ensemble_learning&oldid=1254901523">https://en.wikipedia.org/w/index.php?title=Ensemble_learning&oldid=1254901523</a>"</div></div> <div id="catlinks" class="catlinks" data-mw="interface"><div id="mw-normal-catlinks" class="mw-normal-catlinks"><a href="/wiki/Help:Category" title="Help:Category">Category</a>: <ul><li><a href="/wiki/Category:Ensemble_learning" title="Category:Ensemble learning">Ensemble learning</a></li></ul></div><div id="mw-hidden-catlinks" class="mw-hidden-catlinks mw-hidden-cats-hidden">Hidden categories: <ul><li><a href="/wiki/Category:CS1_maint:_DOI_inactive_as_of_November_2024" title="Category:CS1 maint: DOI inactive as of November 2024">CS1 maint: DOI inactive as of November 2024</a></li><li><a href="/wiki/Category:Articles_with_short_description" title="Category:Articles with short description">Articles with short description</a></li><li><a href="/wiki/Category:Short_description_is_different_from_Wikidata" title="Category:Short description is different from Wikidata">Short description is different from Wikidata</a></li><li><a href="/wiki/Category:All_articles_with_unsourced_statements" title="Category:All articles with unsourced statements">All articles with unsourced statements</a></li><li><a href="/wiki/Category:Articles_with_unsourced_statements_from_December_2017" title="Category:Articles with unsourced statements from December 2017">Articles with unsourced statements from December 2017</a></li><li><a href="/wiki/Category:Articles_with_unsourced_statements_from_January_2012" title="Category:Articles with unsourced statements from January 2012">Articles with unsourced statements from January 2012</a></li></ul></div></div> </div> </main> </div> <div class="mw-footer-container"> <footer id="footer" class="mw-footer" > <ul id="footer-info"> <li id="footer-info-lastmod"> This page was last edited on 2 November 2024, at 06:47<span class="anonymous-show"> (UTC)</span>.</li> <li id="footer-info-copyright">Text is available under the <a href="/wiki/Wikipedia:Text_of_the_Creative_Commons_Attribution-ShareAlike_4.0_International_License" title="Wikipedia:Text of the Creative Commons Attribution-ShareAlike 4.0 International License">Creative Commons Attribution-ShareAlike 4.0 License</a>; additional terms may apply. By using this site, you agree to the <a href="https://foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Terms_of_Use" class="extiw" title="foundation:Special:MyLanguage/Policy:Terms of Use">Terms of Use</a> and <a href="https://foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Privacy_policy" class="extiw" title="foundation:Special:MyLanguage/Policy:Privacy policy">Privacy Policy</a>. Wikipedia® is a registered trademark of the <a rel="nofollow" class="external text" href="https://wikimediafoundation.org/">Wikimedia Foundation, Inc.</a>, a non-profit organization.</li> </ul> <ul id="footer-places"> <li id="footer-places-privacy"><a href="https://foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Privacy_policy">Privacy policy</a></li> <li id="footer-places-about"><a href="/wiki/Wikipedia:About">About Wikipedia</a></li> <li id="footer-places-disclaimers"><a href="/wiki/Wikipedia:General_disclaimer">Disclaimers</a></li> <li id="footer-places-contact"><a href="//en.wikipedia.org/wiki/Wikipedia:Contact_us">Contact Wikipedia</a></li> <li id="footer-places-wm-codeofconduct"><a href="https://foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Universal_Code_of_Conduct">Code of Conduct</a></li> <li id="footer-places-developers"><a href="https://developer.wikimedia.org">Developers</a></li> <li id="footer-places-statslink"><a href="https://stats.wikimedia.org/#/en.wikipedia.org">Statistics</a></li> <li id="footer-places-cookiestatement"><a href="https://foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Cookie_statement">Cookie statement</a></li> <li id="footer-places-mobileview"><a href="//en.m.wikipedia.org/w/index.php?title=Ensemble_learning&mobileaction=toggle_view_mobile" class="noprint stopMobileRedirectToggle">Mobile view</a></li> </ul> <ul id="footer-icons" class="noprint"> <li id="footer-copyrightico"><a href="https://wikimediafoundation.org/" class="cdx-button cdx-button--fake-button cdx-button--size-large cdx-button--fake-button--enabled"><img src="/static/images/footer/wikimedia-button.svg" width="84" height="29" alt="Wikimedia Foundation" loading="lazy"></a></li> <li id="footer-poweredbyico"><a href="https://www.mediawiki.org/" class="cdx-button cdx-button--fake-button cdx-button--size-large cdx-button--fake-button--enabled"><img src="/w/resources/assets/poweredby_mediawiki.svg" alt="Powered by MediaWiki" width="88" height="31" loading="lazy"></a></li> </ul> </footer> </div> </div> </div> <div class="vector-settings" id="p-dock-bottom"> <ul></ul> </div><script>(RLQ=window.RLQ||[]).push(function(){mw.config.set({"wgHostname":"mw-web.codfw.main-f69cdc8f6-rgbsb","wgBackendResponseTime":158,"wgPageParseReport":{"limitreport":{"cputime":"1.215","walltime":"1.639","ppvisitednodes":{"value":5567,"limit":1000000},"postexpandincludesize":{"value":209375,"limit":2097152},"templateargumentsize":{"value":4098,"limit":2097152},"expansiondepth":{"value":12,"limit":100},"expensivefunctioncount":{"value":11,"limit":500},"unstrip-depth":{"value":1,"limit":20},"unstrip-size":{"value":302670,"limit":5000000},"entityaccesscount":{"value":11,"limit":400},"timingprofile":["100.00% 1383.267 1 -total"," 74.17% 1025.901 1 Template:Reflist"," 32.16% 444.877 8 Template:Cite_Q"," 23.46% 324.568 34 Template:Cite_journal"," 8.68% 120.049 19 Template:Cite_book"," 8.49% 117.506 1 Template:Machine_learning"," 7.81% 107.970 1 Template:Sidebar_with_collapsible_lists"," 5.35% 73.956 1 Template:Short_description"," 4.06% 56.151 2 Template:Citation_needed"," 3.47% 47.960 2 Template:Fix"]},"scribunto":{"limitreport-timeusage":{"value":"0.833","limit":"10.000"},"limitreport-memusage":{"value":8341920,"limit":52428800},"limitreport-logs":"1 1 Jennifer A. Hoeting\n2 2 David Madigan\n3 3 Adrian Raftery\n4 4 Chris Volinsky\n1 1 Chris Fraley\n2 2 Adrian Raftery\n3 3 J. McLean Sloughter\n4 4 Tilmann Gneiting\n1 1 Adrian Raftery\n2 2 Jennifer A. Hoeting\n3 3 Chris Volinsky\n4 4 Ian Painter\n5 5 Ka Yee Yeung\n1 1 Adrian Raftery\n1 1 Merlise A. Clyde\n2 2 Michael L. Littman\n3 3 Quanli Wang\n4 4 Joyee Ghosh\n5 5 Yingbo Li\n6 6 Don van den Bergh\n1 1 Gerda Claeskens\n2 2 Nils Lid Hjort\n1 1 Kenneth P. Burnham\n2 2 David R. Anderson\n1 1 Kenneth P. Burnham\n2 2 David R. Anderson\n"},"cachereport":{"origin":"mw-web.codfw.main-f69cdc8f6-csdqc","timestamp":"20241122143954","ttl":2592000,"transientcontent":false}}});});</script> <script type="application/ld+json">{"@context":"https:\/\/schema.org","@type":"Article","name":"Ensemble learning","url":"https:\/\/en.wikipedia.org\/wiki\/Ensemble_learning","sameAs":"http:\/\/www.wikidata.org\/entity\/Q245652","mainEntity":"http:\/\/www.wikidata.org\/entity\/Q245652","author":{"@type":"Organization","name":"Contributors to Wikimedia projects"},"publisher":{"@type":"Organization","name":"Wikimedia Foundation, Inc.","logo":{"@type":"ImageObject","url":"https:\/\/www.wikimedia.org\/static\/images\/wmf-hor-googpub.png"}},"datePublished":"2009-03-30T20:50:15Z","dateModified":"2024-11-02T06:47:51Z","headline":"in machine learning, the use of multiple algorithms to obtain better predictive performance than from any of the constituent learning algorithms alone"}</script> </body> </html>