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Two popular methods to construct an Ensemble are Bagging introduced by Breiman, (1996) and" /> <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/40457872/ON_THE_PERFORMANCE_OF_ENSEMBLES_OF_CLASSIFIERS_BASED_ON_KERNEL_DENSITY_ESTIMATION" /> <meta property="og:title" content="ON THE PERFORMANCE OF ENSEMBLES OF CLASSIFIERS BASED ON KERNEL DENSITY ESTIMATION" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="A combination of classication rules (classiers) is known as an Ensemble, and in general it is more accurate than the individual classiers used to build it. Two popular methods to construct an Ensemble are Bagging introduced by Breiman, (1996) and" /> <meta property="article:author" content="https://uprm.academia.edu/EAcuna" /> <meta name="description" content="A combination of classication rules (classiers) is known as an Ensemble, and in general it is more accurate than the individual classiers used to build it. Two popular methods to construct an Ensemble are Bagging introduced by Breiman, (1996) and" /> <title>(PDF) ON THE PERFORMANCE OF ENSEMBLES OF CLASSIFIERS BASED ON KERNEL DENSITY ESTIMATION</title> <link rel="canonical" href="https://www.academia.edu/40457872/ON_THE_PERFORMANCE_OF_ENSEMBLES_OF_CLASSIFIERS_BASED_ON_KERNEL_DENSITY_ESTIMATION" /> <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(1740259399000); window.Aedu.timeDifference = new Date().getTime() - 1740259399000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"A combination of classi cation rules (classi ers) is known as an Ensemble, and in general it is more accurate than the individual classi ers used to build it. Two popular methods to construct an Ensemble are Bagging introduced by Breiman, (1996) and Boosting (Freund and Schapire, 1996). Both method rely on resamplingtechniques to obtain di erent training sets for each of the classi ers. Previous work has shown that Bagging as well as Boosting are very e ective for unstable classi ers. In this paper we present experimental results of application of both combining techniques using classi ers where the class conditional density is estimated using kernel density estimators. The e ect of sequential forward selection on the performance of the Ensemble also is considered.","author":[{"@context":"https://schema.org","@type":"Person","name":"Edgar Acuna","url":"https://uprm.academia.edu/EAcuna"}],"contributor":[],"dateCreated":"2019-09-27","datePublished":"2003-01-01","headline":"ON THE PERFORMANCE OF ENSEMBLES OF CLASSIFIERS BASED ON KERNEL DENSITY ESTIMATION","image":"https://attachments.academia-assets.com/60722171/thumbnails/1.jpg","inLanguage":"en","keywords":[],"publication":"Proceedings of the International Conference on Computer, Communication and Control Technologies 2003","publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"uprm"}],"thumbnailUrl":"https://attachments.academia-assets.com/60722171/thumbnails/1.jpg","url":"https://www.academia.edu/40457872/ON_THE_PERFORMANCE_OF_ENSEMBLES_OF_CLASSIFIERS_BASED_ON_KERNEL_DENSITY_ESTIMATION"}</script><style type="text/css">@media(max-width: 567px){:root{--token-mode: Rebrand;--dropshadow: 0 2px 4px 0 #22223340;--primary-brand: #0645b1;--error-dark: #b60000;--success-dark: #05b01c;--inactive-fill: #ebebee;--hover: #0c3b8d;--pressed: #082f75;--button-primary-fill-inactive: #ebebee;--button-primary-fill: #0645b1;--button-primary-text: #ffffff;--button-primary-fill-hover: #0c3b8d;--button-primary-fill-press: #082f75;--button-primary-icon: #ffffff;--button-primary-fill-inverse: #ffffff;--button-primary-text-inverse: #082f75;--button-primary-icon-inverse: 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window.loswp.work = {"work":{"id":40457872,"created_at":"2019-09-27T08:39:55.393-07:00","from_world_paper_id":null,"updated_at":"2024-05-15T08:29:23.329-07:00","_data":{"abstract":"A combination of classi\fcation rules (classi\fers) is known as an Ensemble, and in general it is more accurate than the individual classi\fers used to build it. Two popular methods to construct an Ensemble are Bagging introduced by Breiman, (1996) and Boosting (Freund and Schapire, 1996). Both method rely on resamplingtechniques to obtain di\u000berent training sets for each of the classi\fers. Previous work has shown that Bagging as well as Boosting are very e\u000bective for unstable classi\fers. In this paper we present experimental results of application of both combining techniques using classi\fers where the class conditional density is estimated using kernel density estimators. The e\u000bect of sequential forward selection on the performance of the Ensemble also is considered. ","publication_date":"2003,,","publication_name":"Proceedings of the International Conference on Computer, Communication and Control Technologies 2003"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"ON THE PERFORMANCE OF ENSEMBLES OF CLASSIFIERS BASED ON KERNEL DENSITY ESTIMATION","broadcastable":true,"draft":false,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [25040474]; 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":60722171,"attachmentType":"pdf"}"><img alt="First page of “ON THE PERFORMANCE OF ENSEMBLES OF CLASSIFIERS BASED ON KERNEL DENSITY ESTIMATION”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/60722171/mini_magick20190927-2371-s6z5ys.png?1569598811" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.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 PDF</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">ON THE PERFORMANCE OF ENSEMBLES OF CLASSIFIERS BASED ON KERNEL DENSITY ESTIMATION</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="25040474" href="https://uprm.academia.edu/EAcuna"><img alt="Profile image of Edgar Acuna" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Edgar Acuna</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2003, Proceedings of the International Conference on Computer, Communication and Control Technologies 2003</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">description</span><p class="ds2-5-body-sm">6 pages</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 = 40457872; 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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">A combination of classication rules (classiers) is known as an Ensemble, and in general it is more accurate than the individual classiers used to build it. Two popular methods to construct an Ensemble are Bagging introduced by Breiman, (1996) and Boosting (Freund and Schapire, 1996). Both method rely on resamplingtechniques to obtain dierent training sets for each of the classiers. Previous work has shown that Bagging as well as Boosting are very eective for unstable classiers. In this paper we present experimental results of application of both combining techniques using classiers where the class conditional density is estimated using kernel density estimators. The eect of sequential forward selection on the performance of the Ensemble also is considered. </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":60722171,"attachmentType":"pdf","workUrl":"https://www.academia.edu/40457872/ON_THE_PERFORMANCE_OF_ENSEMBLES_OF_CLASSIFIERS_BASED_ON_KERNEL_DENSITY_ESTIMATION"}">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":60722171,"attachmentType":"pdf","workUrl":"https://www.academia.edu/40457872/ON_THE_PERFORMANCE_OF_ENSEMBLES_OF_CLASSIFIERS_BASED_ON_KERNEL_DENSITY_ESTIMATION"}"><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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Three popular methods for creating ensembles are Bagging (Bootstrap Aggregating), AdaBoosting (Adaptive Boosting) and Arcing (Adaptively resample and combine). These methods rely on resampling techniques to obtain different training sets for each of the classifiers. Stacking is another method to combine classifiers but it does not use resampling. Previous work has demonstrated that combining techniques are very effective for unstable classifiers. In this paper will present some results in application of combining techniques to classifiers where the class conditional density is estimated using Kernel density estimators. These type of classifiers are unstable due to singularities presented in the log-likelihood function. Also, we will explore the mixed kernel estimators and adaptive kernel density estimators.</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":"Ensembles of classifiers based on Kernel density estimators","attachmentId":41737566,"attachmentType":"pdf","work_url":"https://www.academia.edu/21150882/Ensembles_of_classifiers_based_on_Kernel_density_estimators","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/21150882/Ensembles_of_classifiers_based_on_Kernel_density_estimators"><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="21150903" 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/21150903/Bagging_Classifiers_Based_on_Kernel_Density_Estimators">Bagging Classifiers Based on Kernel Density Estimators</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="9745989" href="https://uprm.academia.edu/EdgarAcuna">Edgar Acuna</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Bagging, a method for voting classification algorithms, has been shown to be a useful tool for improving the predictive power of classifiers learning systems [12]. In this paper, we review this algorithm and carry out an empirical study comparing this method using classifiers based on kernel density estimators on a representative collection of datasets.</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 Classifiers Based on Kernel Density Estimators","attachmentId":41737576,"attachmentType":"pdf","work_url":"https://www.academia.edu/21150903/Bagging_Classifiers_Based_on_Kernel_Density_Estimators","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/21150903/Bagging_Classifiers_Based_on_Kernel_Density_Estimators"><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="26293720" 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/26293720/New_Applications_of_Ensembles_of_Classifiers">New Applications of Ensembles of 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="30528773" href="https://uaemex.academia.edu/RosaValdovinos">Rosa Valdovinos</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Pattern Analysis & Applications, 2003</p><p class="ds-related-work--abstract ds2-5-body-sm">Combination (ensembles) of classifiers is now a well established research line. It has been observed that the predictive accuracy of a combination of independent classifiers excels that of the single best classifier. While ensembles of classifiers have been mostly employed to achieve higher recognition accuracy, this paper focuses on the use of combinations of individual classifiers for handling several problems from the practice in the machine learning, pattern recognition and data mining domains. In particular, the study presented concentrates on managing the imbalanced training sample problem, scaling up some preprocessing algorithms and filtering the training set. Here, all these situations are examined mainly in connection with the nearest neighbour classifier. Experimental results show the potential of multiple classifier systems when applied to those situations.</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":"New Applications of Ensembles of Classifiers","attachmentId":46606995,"attachmentType":"pdf","work_url":"https://www.academia.edu/26293720/New_Applications_of_Ensembles_of_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/26293720/New_Applications_of_Ensembles_of_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="3" data-entity-id="67308784" 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/67308784/An_empirical_evaluation_of_bagging_and_boosting">An empirical evaluation of bagging and boosting</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">… CONFERENCE ON ARTIFICIAL INTELLIGENCE, 1997</p><p class="ds-related-work--abstract ds2-5-body-sm">An ensemble consists of a set of independently trained classi ers (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 classiers 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 classi cation algorithms. Our results clearly show two important facts. The rst is that even though Bagging almost always produces a better classi er than any of its individual component classi ers and is relatively impervious to over tting, 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 susceptible to noise and can quickly over t a 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 evaluation of bagging and boosting","attachmentId":78173773,"attachmentType":"pdf","work_url":"https://www.academia.edu/67308784/An_empirical_evaluation_of_bagging_and_boosting","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/67308784/An_empirical_evaluation_of_bagging_and_boosting"><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="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="5" data-entity-id="39060549" 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/39060549/An_Empirical_Study_of_Ensemble_Techniques_Bagging_Boosting_and_Stacking">An Empirical Study of Ensemble Techniques (Bagging, Boosting and Stacking</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="16481084" href="https://uekunwe.academia.edu/RisingOdegua">Rising Odegua</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2019</p><p class="ds-related-work--abstract ds2-5-body-sm">Ensemble methods are popular strategies for improving the predictive ability of a machine learning model. 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="6" 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. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. 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="7" 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="8" data-entity-id="4403890" 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/4403890/Empirical_analysis_of_support_vector_machine_ensemble_classifiers">Empirical analysis of support vector machine ensemble 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="5400423" href="https://independent.academia.edu/AvinJay">Avin Jay</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Expert Systems With Applications, 2009</p><p class="ds-related-work--abstract ds2-5-body-sm">2009) Empirical analysis of support vector machine ensemble classifiers. Expert Systems with Applications, 36(3, Part 2). pp. 6466-6476.</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":"Empirical analysis of support vector machine ensemble classifiers","attachmentId":49878431,"attachmentType":"pdf","work_url":"https://www.academia.edu/4403890/Empirical_analysis_of_support_vector_machine_ensemble_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/4403890/Empirical_analysis_of_support_vector_machine_ensemble_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="9" data-entity-id="14835175" 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/14835175/A_New_Method_for_Constructing_Classifier_Ensembles">A New Method for Constructing Classifier 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="33794563" href="https://iust.academia.edu/HoseinAlizadeh">Hosein Alizadeh</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="14152602" href="https://iust.academia.edu/BehroozMinaei">Behrooz Minaei</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2009</p><p class="ds-related-work--abstract ds2-5-body-sm">Usage of recognition systems has found many applications in almost all fields. However, Most of classification algorithms have obtained good performance for specific problems; they have not enough robustness for other problems. Combination of multiple classifiers can be considered as a general solution method for pattern recognition problems. It has been shown that combination of classifiers can usually operate better than single classifier provided that its components are independent or they have diverse outputs. It was shown that the necessary diversity of an ensemble can be achieved manipulation of data set features. We also propose a new method of creating this diversity. The ensemble created by proposed method may not always outperforms all classifiers existing in it, it is always possesses the diversity needed for creation of ensemble, and consequently it always outperforms the simple classifier.</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":"A New Method for Constructing Classifier Ensembles","attachmentId":43861646,"attachmentType":"pdf","work_url":"https://www.academia.edu/14835175/A_New_Method_for_Constructing_Classifier_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/14835175/A_New_Method_for_Constructing_Classifier_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></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":60722171,"attachmentType":"pdf","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":60722171,"attachmentType":"pdf","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_60722171" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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