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<!DOCTYPE html> <html > <head> <meta charset="utf-8"> <meta rel="search" type="application/opensearchdescription+xml" href="/open_search.xml" title="Academia.edu"> <meta content="width=device-width, initial-scale=1" name="viewport"> <meta name="google-site-verification" content="bKJMBZA7E43xhDOopFZkssMMkBRjvYERV-NaN4R6mrs"> <meta name="csrf-param" content="authenticity_token" /> <meta name="csrf-token" content="M9dgdjRAJB7yd1wR0SYaaEShg9vzf_te8Egi2WRVMgvEU8RQI7ktMlsbQvtMS_a8lqmi1C-ycgQxVqPjoQVpsQ" /> <meta name="citation_title" content="An Empirical Evaluation of Bagging and Boosting" /> <meta name="citation_publication_date" content="1997/01/01" /> <meta name="citation_author" content="Richard Maclin" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/67308838/An_Empirical_Evaluation_of_Bagging_and_Boosting" /> <meta name="twitter:title" content="An Empirical Evaluation of Bagging and Boosting" /> <meta name="twitter:description" content="An ensemble consists of a set of independently trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more" /> <meta name="twitter:image" content="http://a.academia-assets.com/images/twitter-card.jpeg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/67308838/An_Empirical_Evaluation_of_Bagging_and_Boosting" /> <meta property="og:title" content="An Empirical Evaluation of Bagging and Boosting" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="An ensemble consists of a set of independently trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more" /> <meta property="article:author" content="https://independent.academia.edu/RMaclin" /> <meta name="description" content="An ensemble consists of a set of independently trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more" /> <title>An Empirical Evaluation of Bagging and Boosting</title> <link rel="canonical" href="https://www.academia.edu/67308838/An_Empirical_Evaluation_of_Bagging_and_Boosting" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '29cbb9485f79f49ca3eb38b6f0905739256f19a4'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1740202991000); window.Aedu.timeDifference = new Date().getTime() - 1740202991000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"An ensemble consists of a set of independently trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman 1996a) and Boosting (Freund \u0026amp;amp; Schapire 1996) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods using both neural networks and decision trees as our classification algorithms. Our results clearly show two important facts. The first is that even though Bagging almost always produces a better classifier than any of its individual component classifiers and is relatively impervious to overfitting, it does not generalize any better than a baseline neural-network ensemble method. The second is that Boosting is a powerful technique that can usually produce better ensembles than Bagging; however, it is more susceptib...","author":[{"@context":"https://schema.org","@type":"Person","name":"Richard Maclin","url":"https://independent.academia.edu/RMaclin"}],"contributor":[],"dateCreated":"2022-01-05","dateModified":"2022-01-05","datePublished":"1997-01-01","headline":"An Empirical Evaluation of Bagging and Boosting","image":"https://a.academia-assets.com/images/blank-paper.jpg","inLanguage":"en","keywords":["Computer Science","Empirical Evaluation"],"publisher":{"@context":"https://schema.org","@type":"Organization","name":"AAAI/IAAI"},"sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":null}],"thumbnailUrl":"https://a.academia-assets.com/images/blank-paper.jpg","url":"https://www.academia.edu/67308838/An_Empirical_Evaluation_of_Bagging_and_Boosting"}</script><style type="text/css">@media(max-width: 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{"work":{"id":67308838,"created_at":"2022-01-05T20:02:11.156-08:00","from_world_paper_id":190779301,"updated_at":"2022-01-05T20:24:18.730-08:00","_data":{"abstract":"An ensemble consists of a set of independently trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman 1996a) and Boosting (Freund \u0026 Schapire 1996) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods using both neural networks and decision trees as our classification algorithms. Our results clearly show two important facts. The first is that even though Bagging almost always produces a better classifier than any of its individual component classifiers and is relatively impervious to overfitting, it does not generalize any better than a baseline neural-network ensemble method. The second is that Boosting is a powerful technique that can usually produce better ensembles than Bagging; however, it is more susceptib...","publisher":"AAAI/IAAI","publication_date":"1997,,"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"An Empirical Evaluation of Bagging and Boosting","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [155758897]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":78173703,"attachmentType":"eps"}"><div class="ds-work-cover--blank-cover"><div class="ds-work-cover--blank-cover--title">An Empirical Evaluation of Bagging and Boosting</div></div><img alt="Academia Logo" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/academia_logo.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free EPS</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">An Empirical Evaluation of Bagging and Boosting</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="155758897" href="https://independent.academia.edu/RMaclin"><img alt="Profile image of Richard Maclin" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Richard Maclin</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">1997</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 67308838; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=67308838"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { try { const viewCountNumber = parseInt(viewCount, 10); if (viewCountNumber === 0) { // Remove the whole views element if there are zero views. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); return; } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">An ensemble consists of a set of independently trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman 1996a) and Boosting (Freund & Schapire 1996) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods using both neural networks and decision trees as our classification algorithms. Our results clearly show two important facts. The first is that even though Bagging almost always produces a better classifier than any of its individual component classifiers and is relatively impervious to overfitting, it does not generalize any better than a baseline neural-network ensemble method. The second is that Boosting is a powerful technique that can usually produce better ensembles than Bagging; however, it is more susceptib...</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":78173703,"attachmentType":"eps","workUrl":"https://www.academia.edu/67308838/An_Empirical_Evaluation_of_Bagging_and_Boosting"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":78173703,"attachmentType":"eps","workUrl":"https://www.academia.edu/67308838/An_Empirical_Evaluation_of_Bagging_and_Boosting"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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An ensemble consists of a set of individually trained base learners/models whose predictions are combined when classifying new cases. Previous researches have shown that an ensemble is on the average more accurate than a single base model. Bagging, Boosting and Stacking are some popular ensemble techniques which we studied in this paper. We evaluated these ensembles on 9 data sets. From our results, we observed the following. First, an ensemble is always more accurate than a single base model. Secondly, we observed that Boosting ensembles is on the average better than Bagging while Stacking (meta-learning) is on the average more accurate than Boosting and Bagging. Further experiment also shows that the gain in predictive power of any ensembles may sometimes be small or even decrease depending on the data set.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"An Empirical Study of Ensemble Techniques (Bagging, Boosting and Stacking","attachmentId":59175303,"attachmentType":"pdf","work_url":"https://www.academia.edu/39060549/An_Empirical_Study_of_Ensemble_Techniques_Bagging_Boosting_and_Stacking","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/39060549/An_Empirical_Study_of_Ensemble_Techniques_Bagging_Boosting_and_Stacking"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="67308829" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/67308829/Popular_Ensemble_Methods_An_Empirical_Study">Popular Ensemble Methods: An Empirical Study</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="155758897" href="https://independent.academia.edu/RMaclin">Richard Maclin</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Artificial Intelligence Research</p><p class="ds-related-work--abstract ds2-5-body-sm">An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. 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Analysis indicates that the performance of the Boosting methods is dependent on the characteristics ...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Popular Ensemble Methods: An Empirical Study","attachmentId":78173691,"attachmentType":"pdf","work_url":"https://www.academia.edu/67308829/Popular_Ensemble_Methods_An_Empirical_Study","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/67308829/Popular_Ensemble_Methods_An_Empirical_Study"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="2179973" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/2179973/An_Empirical_Comparison_of_Boosting_and_Bagging_Algorithms">An Empirical Comparison of Boosting and Bagging Algorithms</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2798465" href="https://independent.academia.edu/KalaichelviChandrahasan">Kalaichelvi Chandrahasan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of …, 2011</p><p class="ds-related-work--abstract ds2-5-body-sm">Classification is one of the data mining techniques that analyses a given data set and induces a model for each class based on their features present in the data. Bagging and boosting are heuristic approaches to develop classification models. These techniques generate a diverse ensemble of classifiers by manipulating the training data given to a base learning algorithm. They are very successful in improving the accuracy of some algorithms in artificial and real world datasets. We review the algorithms such as AdaBoost, Bagging, ADTree, and Random Forest in conjunction with the Meta classifier and the Decision Tree classifier. Also we describe a large empirical study by comparing several variants. The algorithms are analyzed on Accuracy, Precision, Error Rate and Execution Time.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"An Empirical Comparison of Boosting and Bagging Algorithms","attachmentId":30966397,"attachmentType":"pdf","work_url":"https://www.academia.edu/2179973/An_Empirical_Comparison_of_Boosting_and_Bagging_Algorithms","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/2179973/An_Empirical_Comparison_of_Boosting_and_Bagging_Algorithms"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="40229697" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/40229697/The_Superiority_of_the_Ensemble_Classification_Methods_A_Comprehensive_Review">The Superiority of the Ensemble Classification Methods: A Comprehensive Review</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="25490016" href="https://jkuat.academia.edu/SilasNzuva">Silas M Nzuva</a></div><p class="ds-related-work--abstract ds2-5-body-sm">The modern technologies, which are characterized by cyber-physical systems and internet of things expose organizations to big data, which in turn can be processed to derive actionable knowledge. Machine learning techniques have vastly been employed in both supervised and unsupervised environments in an effort to develop systems that are capable of making feasible decisions in light of past data. In order to enhance the accuracy of supervised learning algorithms, various classification-based ensemble methods have been developed. Herein, we review the superiority exhibited by ensemble learning algorithms based on the past that has been carried out over the years. Moreover, we proceed to compare and discuss the common classification-based ensemble methods, with an emphasis on the boosting and bagging ensemble-learning models. We conclude by out setting the superiority of the ensemble learning models over individual base learners.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"The Superiority of the Ensemble Classification Methods: A Comprehensive Review","attachmentId":60459918,"attachmentType":"pdf","work_url":"https://www.academia.edu/40229697/The_Superiority_of_the_Ensemble_Classification_Methods_A_Comprehensive_Review","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/40229697/The_Superiority_of_the_Ensemble_Classification_Methods_A_Comprehensive_Review"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="38811755" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/38811755/Comparison_of_Bagging_and_Voting_Ensemble_Machine_Learning_Algorithm_as_a_Classifier">Comparison of Bagging and Voting Ensemble Machine Learning Algorithm as a Classifier</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="13056089" href="https://iauoe.academia.edu/ugochukwuonwuka">ugochukwu onwuka</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journals of Advanced Research in Computer Science and Software Engineering ISSN: 2277-128X (Volume-9, Issue-3), 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">Bagging and Voting are both types of ensemble learning, which is a type of machine learning where multiple classifiers are combined to get better classification results. This paper presents an experimental comparison of Bagging and Voting ensemble machine learning algorithms. The iris dataset which has 150 data instances and 5 attributes was used to conduct the experiment. It was observed that bagging is a better ensemble learning algorithm than voting based on the experimental data used for classification.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Comparison of Bagging and Voting Ensemble Machine Learning Algorithm as a Classifier","attachmentId":58903613,"attachmentType":"pdf","work_url":"https://www.academia.edu/38811755/Comparison_of_Bagging_and_Voting_Ensemble_Machine_Learning_Algorithm_as_a_Classifier","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/38811755/Comparison_of_Bagging_and_Voting_Ensemble_Machine_Learning_Algorithm_as_a_Classifier"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="3805983" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/3805983/Using_boosting_to_prune_bagging_ensembles">Using boosting to prune bagging ensembles</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="4680607" href="https://independent.academia.edu/GonzaloMu%C3%B1oz3">Gonzalo Muñoz</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Pattern Recognition Letters, 2007</p><p class="ds-related-work--abstract ds2-5-body-sm">Boosting is used to determine the order in which classifiers are aggregated in a bagging ensemble. Early stopping in the aggregation of the classifiers in the ordered bagging ensemble allows the identification of subensembles that require less memory for storage, classify faster and can improve the generalization accuracy of the original bagging ensemble. In all the classification problems investigated pruned ensembles with 20% of the original classifiers show statistically significant improvements over bagging. In problems where boosting is superior to bagging, these improvements are not sufficient to reach the accuracy of the corresponding boosting ensembles. However, ensemble pruning preserves the performance of bagging in noisy classification tasks, where boosting often has larger generalization errors. Therefore, pruned bagging should generally be preferred to complete bagging and, if no information about the level of noise is available, it is a robust alternative to AdaBoost.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Using boosting to prune bagging ensembles","attachmentId":50130335,"attachmentType":"pdf","work_url":"https://www.academia.edu/3805983/Using_boosting_to_prune_bagging_ensembles","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/3805983/Using_boosting_to_prune_bagging_ensembles"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="14032097" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/14032097/An_experimental_study_on_diversity_for_bagging_and_boosting_with_linear_classifiers">An experimental study on diversity for bagging and boosting with linear classifiers</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="33059519" href="https://bangor.academia.edu/LudmilaKuncheva">Ludmila Kuncheva</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2002</p><p class="ds-related-work--abstract ds2-5-body-sm">In classifier combination, it is believed that diverse ensembles have a better potential for improvement on the accuracy than nondiverse ensembles. We put this hypothesis to a test for two methods for building the ensembles: Bagging and Boosting, with two linear classifier models: the nearest mean classifier and the pseudo-Fisher linear discriminant classifier. To estimate diversity, we apply nine measures proposed in the recent literature on combining classifiers. Eight combination methods were used: minimum, maximum, product, average, simple majority, weighted majority, Naive Bayes and decision templates. We carried out experiments on seven data sets for different sample sizes, different number of classifiers in the ensembles, and the two linear classifiers. Altogether, we created 1364 ensembles by the Bagging method and the same number by the Boosting method. On each of these, we calculated the nine measures of diversity and the accuracy of the eight different combination methods, averaged over 50 runs. The results confirmed in a quantitative way the intuitive explanation behind the success of Boosting for linear classifiers for increasing training sizes, and the poor performance of Bagging in this case. Diversity measures indicated that Boosting succeeds in inducing diversity even for stable classifiers whereas Bagging does not.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"An experimental study on diversity for bagging and boosting with linear classifiers","attachmentId":44684247,"attachmentType":"pdf","work_url":"https://www.academia.edu/14032097/An_experimental_study_on_diversity_for_bagging_and_boosting_with_linear_classifiers","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/14032097/An_experimental_study_on_diversity_for_bagging_and_boosting_with_linear_classifiers"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="114017427" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/114017427/Improving_ensemble_decision_tree_performance_using_Adaboost_and_Bagging">Improving ensemble decision tree performance using Adaboost and Bagging</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="278576605" href="https://iutoic-dhaka.academia.edu/OPENREAD">OPEN READ</a></div><p class="ds-related-work--metadata ds2-5-body-xs">AIP Conference Proceedings, 2015</p><p class="ds-related-work--abstract ds2-5-body-sm">Ensemble classifier systems are considered as one of the most promising in medical data classification and the performance of deceision tree classifier can be increased by the ensemble method as it is proven to be better than single classifiers. However, in a ensemble settings the performance depends on the selection of suitable base classifier. This research employed two prominent esemble s namely Adaboost and Bagging with base classifiers such as Random Forest, Random Tree, j48, j48grafts and Logistic Model Regression (LMT) that have been selected independently. The empirical study shows that the performance varries when different base classifiers are selected and even some places overfitting issue also been noted. The evidence shows that ensemble decision tree classfiers using Adaboost and Bagging improves the performance of selected medical data sets.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Improving ensemble decision tree performance using Adaboost and Bagging","attachmentId":110826601,"attachmentType":"pdf","work_url":"https://www.academia.edu/114017427/Improving_ensemble_decision_tree_performance_using_Adaboost_and_Bagging","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/114017427/Improving_ensemble_decision_tree_performance_using_Adaboost_and_Bagging"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="74401656" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/74401656/Advanced_Methodologies_Employed_in_Ensemble_of_Classifiers_A_Survey">Advanced Methodologies Employed in Ensemble of Classifiers : A Survey</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="9497196" href="https://independent.academia.edu/MrsMadhaviPradhan">Mrs Madhavi Pradhan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2014</p><p class="ds-related-work--abstract ds2-5-body-sm">If we look a few years back, we will find that ensemble classification model has outbreak many research and publication in the data mining community discussing how to combine models or model prediction with reduction in the error that results. When we ensemble the prediction of more than one classifier, more accurate and robust models are generated. We have convention that bagging, boosting with neural network etc. are the most popular method of combining different models and are realized in many data mining software but there are variation and alternative to bagging and boosting. This survey paper will give insight into various newly proposed ensemble classification models based on different methodologies.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Advanced Methodologies Employed in Ensemble of Classifiers : A Survey","attachmentId":82567650,"attachmentType":"pdf","work_url":"https://www.academia.edu/74401656/Advanced_Methodologies_Employed_in_Ensemble_of_Classifiers_A_Survey","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/74401656/Advanced_Methodologies_Employed_in_Ensemble_of_Classifiers_A_Survey"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="88781503" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/88781503/adabag_AnRPackage_for_Classification_with_Boosting_and_Bagging">adabag: AnRPackage for Classification with Boosting and Bagging</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="50748033" href="https://independent.academia.edu/NoeliaRubio1">Noelia Rubio</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Statistical Software, 2013</p><p class="ds-related-work--abstract ds2-5-body-sm">Boosting and bagging are two widely used ensemble methods for classification. Their common goal is to improve the accuracy of a classifier combining single classifiers which are slightly better than random guessing. Among the family of boosting algorithms, AdaBoost (adaptive boosting) is the best known, although it is suitable only for dichotomous tasks. AdaBoost.M1 and SAMME (stagewise additive modeling using a multi-class exponential loss function) are two easy and natural extensions to the general case of two or more classes. In this paper, the adabag R package is introduced. This version implements AdaBoost.M1, SAMME and bagging algorithms with classification trees as base classifiers. Once the ensembles have been trained, they can be used to predict the class of new samples. The accuracy of these classifiers can be estimated in a separated data set or through cross validation. Moreover, the evolution of the error as the ensemble grows can be analysed and the ensemble can be pruned. In addition, the margin in the class prediction and the probability of each class for the observations can be calculated. Finally, several classic examples in classification literature are shown to illustrate the use of this package.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"adabag: AnRPackage for Classification with Boosting and Bagging","attachmentId":92692526,"attachmentType":"pdf","work_url":"https://www.academia.edu/88781503/adabag_AnRPackage_for_Classification_with_Boosting_and_Bagging","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/88781503/adabag_AnRPackage_for_Classification_with_Boosting_and_Bagging"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":78173703,"attachmentType":"eps","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":78173703,"attachmentType":"eps","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_78173703" style="display: none"><div class="scribd--being-converted-container">This document is currently being converted. Please check back in a few minutes.</div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="8157655" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/8157655/REVIEW_OF_ENSEMBLE_CLASSIFICATION">REVIEW OF ENSEMBLE CLASSIFICATION</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="7009884" href="https://cmscbe.academia.edu/chiraj">chi raj</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"REVIEW OF ENSEMBLE 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href="https://www.academia.edu/74954528/L_Experimental_comparison_between_bagging_and_Monte_Carlo_ensemble_classification">L.: Experimental comparison between bagging and Monte Carlo ensemble classification</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="33097014" href="https://unipmn.academia.edu/LSaitta">Lorenza Saitta</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2005</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"L.: Experimental comparison between bagging and Monte Carlo ensemble classification","attachmentId":82919849,"attachmentType":"pdf","work_url":"https://www.academia.edu/74954528/L_Experimental_comparison_between_bagging_and_Monte_Carlo_ensemble_classification","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/74954528/L_Experimental_comparison_between_bagging_and_Monte_Carlo_ensemble_classification"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="2" data-entity-id="102789633" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/102789633/Predictive_Ensemble_Modelling_Experimental_Comparison_of_Boosting_Implementation_Methods">Predictive Ensemble Modelling: Experimental Comparison of Boosting Implementation Methods</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="178401409" href="https://independent.academia.edu/EBanissi">Ebad Banissi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2017 European Modelling Symposium (EMS), 2017</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Predictive Ensemble Modelling: Experimental Comparison of Boosting Implementation Methods","attachmentId":102966260,"attachmentType":"pdf","work_url":"https://www.academia.edu/102789633/Predictive_Ensemble_Modelling_Experimental_Comparison_of_Boosting_Implementation_Methods","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/102789633/Predictive_Ensemble_Modelling_Experimental_Comparison_of_Boosting_Implementation_Methods"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="3" data-entity-id="24371142" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/24371142/Tree_Based_Ensemble_Classifiers_for_High_Dimensional_Data">Tree-Based Ensemble Classifiers for High-Dimensional Data</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="46983591" href="https://csulb.academia.edu/HojinMoon">Hojin Moon</a></div><div 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js-related-work-sidebar-card" data-collection-position="4" data-entity-id="5025587" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/5025587/Ensemble_Methods_in_Data_Mining_Improving_Accuracy_Through_Combining_Predictions_BOOK">Ensemble Methods in Data Mining Improving Accuracy Through Combining Predictions BOOK</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="6681686" href="https://hnu.academia.edu/HassanaMaigary">Hassana Maigary</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Ensemble Methods in Data Mining Improving Accuracy Through Combining Predictions 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ds2-5-body-link" href="https://www.academia.edu/20369491/Improving_Classification_Accuracy_through_ensemble_technique_in_Data_Mining">Improving Classification Accuracy through ensemble technique in Data Mining</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="34217662" href="https://technoscienceacademy.academia.edu/IJSRSET">International Journal of Scientific Research in Science, Engineering and Technology IJSRSET</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Improving Classification Accuracy through ensemble technique in Data Mining","attachmentId":41319490,"attachmentType":"pdf","work_url":"https://www.academia.edu/20369491/Improving_Classification_Accuracy_through_ensemble_technique_in_Data_Mining","alternativeTracking":true}"><span 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ds2-5-body-link" data-author-id="38745995" href="https://independent.academia.edu/PB%C3%BChlmann">Peter Bühlmann</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Handbook of Computational Statistics, 2011</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Bagging, Boosting and Ensemble Methods","attachmentId":74117104,"attachmentType":"pdf","work_url":"https://www.academia.edu/60853185/Bagging_Boosting_and_Ensemble_Methods","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/60853185/Bagging_Boosting_and_Ensemble_Methods"><span class="ds2-5-text-link__content">View 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