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(PDF) The Superiority of the Ensemble Classification Methods: A Comprehensive Review
<!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="FhpSkslr4uhJSV5wvHhfK5a-uEJEiiIPFOP95AHS0nRvDUBVcHdbiRyp4jiCrQ9MKFZJ3RYN0u7kAlnAPGip-g" /> <meta name="citation_title" content="The Superiority of the Ensemble Classification Methods: A Comprehensive Review" /> <meta name="citation_author" content="Silas M Nzuva" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/40229697/The_Superiority_of_the_Ensemble_Classification_Methods_A_Comprehensive_Review" /> <meta name="twitter:title" content="The Superiority of the Ensemble Classification Methods: A Comprehensive Review" /> <meta name="twitter:description" content="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" /> <meta name="twitter:image" content="https://0.academia-photos.com/25490016/7653467/47457519/s200_nzuva.silas.jpg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/40229697/The_Superiority_of_the_Ensemble_Classification_Methods_A_Comprehensive_Review" /> <meta property="og:title" content="The Superiority of the Ensemble Classification Methods: A Comprehensive Review" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="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" /> <meta property="article:author" content="https://jkuat.academia.edu/SilasNzuva" /> <meta name="description" content="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" /> <title>(PDF) The Superiority of the Ensemble Classification Methods: A Comprehensive Review</title> <link rel="canonical" href="https://www.academia.edu/40229697/The_Superiority_of_the_Ensemble_Classification_Methods_A_Comprehensive_Review" /> <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 = '1363409c2f6920b9f6129801bf9c25a45dc9d4a3'; 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(1740145488000); window.Aedu.timeDifference = new Date().getTime() - 1740145488000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"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.","author":[{"@context":"https://schema.org","@type":"Person","name":"Silas M Nzuva","url":"https://jkuat.academia.edu/SilasNzuva"}],"contributor":[],"dateCreated":"2019-09-01","dateModified":"2024-07-28","headline":"The Superiority of the Ensemble Classification Methods: A Comprehensive Review","identifier":{"@type":"PropertyValue","propertyID":"DOI","value":"10.7176/JIEA/9-5-05"},"image":"https://attachments.academia-assets.com/60459918/thumbnails/1.jpg","inLanguage":"en","keywords":["Computer Science"],"publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"sameAs":"https://doi.org/10.7176/JIEA/9-5-05","sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"jkuat"}],"thumbnailUrl":"https://attachments.academia-assets.com/60459918/thumbnails/1.jpg","url":"https://www.academia.edu/40229697/The_Superiority_of_the_Ensemble_Classification_Methods_A_Comprehensive_Review"}</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: 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window.loswp.work = {"work":{"id":40229697,"created_at":"2019-09-01T23:40:28.604-07:00","from_world_paper_id":null,"updated_at":"2021-01-16T10:41:20.998-08:00","_data":{"doi":"10.7176/JIEA/9-5-05","abstract":"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."},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"The Superiority of the Ensemble Classification Methods: A Comprehensive Review","broadcastable":true,"draft":false,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [25490016]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; 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":60459918,"attachmentType":"pdf"}"><img alt="First page of “The Superiority of the Ensemble Classification Methods: A Comprehensive Review”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/60459918/mini_magick20190901-22740-o2c2qw.png?1567406455" /><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">The Superiority of the Ensemble Classification Methods: A Comprehensive Review</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="25490016" href="https://jkuat.academia.edu/SilasNzuva"><img alt="Profile image of Silas M Nzuva" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/25490016/7653467/47457519/s65_nzuva.silas.jpg" />Silas M Nzuva</a></div><div class="ds-work-card--detail"><a class="js-loswp-work-card-doi-link ds2-5-body-sm ds2-5-body-link" href="https://doi.org/10.7176/JIEA/9-5-05" rel="nofollow">https://doi.org/10.7176/JIEA/9-5-05</a><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">11 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 = 40229697; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=40229697"; 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">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-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":60459918,"attachmentType":"pdf","workUrl":"https://www.academia.edu/40229697/The_Superiority_of_the_Ensemble_Classification_Methods_A_Comprehensive_Review"}">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":60459918,"attachmentType":"pdf","workUrl":"https://www.academia.edu/40229697/The_Superiority_of_the_Ensemble_Classification_Methods_A_Comprehensive_Review"}"><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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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="1" data-entity-id="8157655" 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/8157655/REVIEW_OF_ENSEMBLE_CLASSIFICATION">REVIEW OF ENSEMBLE CLASSIFICATION</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="7009884" href="https://cmscbe.academia.edu/chiraj">chi raj</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Data mining techniques like classification is effectively for used for prediction. Due to technological up gradation, the datasets which are large are distributed over different locations and classification has become a difficult task. The single classifier models are not sufficient for these types of datasets. So the recent research concentrates on combination of various classifiers and creates models. Ensemble methods combine multiple models and are useful in both supervised and unsupervised learning. This paper discusses the framework of ensemble and two types of ensemble models. A review of various algorithms of these two models is given. Combination methods which are used for combining outputs and few applications where it can be used effectively are also discussed. Key TermsData mining techniques like classification is effectively for used for prediction. Due to technological up gradation, the datasets which are large are distributed over different locations and classification has become a difficult task. The single classifier models are not sufficient for these types of datasets. So the recent research concentrates on combination of various classifiers and creates models. Ensemble methods combine multiple models and are useful in both supervised and unsupervised learning. This paper discusses the framework of ensemble and two types of ensemble models. A review of various algorithms of these two models is given. Combination methods which are used for combining outputs and few applications where it can be used effectively are also discussed.</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":"REVIEW OF ENSEMBLE CLASSIFICATION","attachmentId":34595428,"attachmentType":"pdf","work_url":"https://www.academia.edu/8157655/REVIEW_OF_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-wsj-grid-card-view-pdf" href="https://www.academia.edu/8157655/REVIEW_OF_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-wsj-grid-card" data-collection-position="2" data-entity-id="92594515" 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/92594515/A_Survey_of_Ensemble_Learning_Concepts_Algorithms_Applications_and_Prospects">A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects</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="56002972" href="https://johannesburg.academia.edu/YanxiaSun">Yanxia Sun</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE Access</p><p class="ds-related-work--abstract ds2-5-body-sm">The recent advances in computing power and machine learn-13 ing (ML) have given rise to several innovations and devel-14 opments in many areas of research and human lives. In the 15 last few years, advancements in machine learning, a subset 16 of artificial intelligence (AI), have transformed and inher-17 ently changed almost every area of our lives [1], [2]. Par-18 ticularly, ML has been applied in disease diagnosis [3], 19 fraud detection [4], text classification [5], and image recog-20 nition [6], among others. Unlike humans, ML algorithms 21 consider several factors when making decisions, and they 22 are not prone to fatigue or prejudice. Meanwhile, learn-23 ing can sometimes be challenging, especially when learning 24 from high-dimensional and imbalanced datasets [7],[8], [9]. 25 Research has shown that conventional ML algorithms tend 26 to underperform when trained with imbalanced datasets [10]. 27 Therefore, researchers have frequently resorted to new and 28 improved learning approaches, such as ensemble and deep 29 learning. 30 weighted majority voting is presented in [57]. Meanwhile, 230 there are several combination rules in the literature. In [58], 231 some combination rules were introduced, including the min-232 imum, maximum, product, median, and sum rules. 233 B. ENSEMBLE SELECTION 234 Ensemble selection is a technique used to build ensemble 235 classifiers from a set of base models. It is a vital topic in 236 ensemble learning because selecting a suitable subset of base 237 models could lead to better performance than when all the 238 models are used to construct the ensemble classifier [59]. 239 Since the base models are developed using various ML algo-240 rithms or different subsets of the training data, their perfor-241 mances would be different; while some would have good 242 performance, others might have poor performance. Instead 243 of combining the good and bad models, selecting only a 244 subset of models with good performance might be beneficial, 245 which would enhance the overall ensemble performance [60]. 246 An ensemble selection strategy is employed to select the 247 optimal subset of base classifiers, and they are usually guided 248 by a scoring function [61]. Caruana et al. [60] developed the 249 foremost forward model selection strategy to extract the best 250 performing subset of base models, and its basic procedure is 251 outlined as follows: 252 i. Begin with the empty ensemble. 253 ii. Select the base classifier from the library that maximizes 254 the ensemble's performance using a validation set. 255 iii. Repeat step II for a predefined number of iterations or 256 until all the models in the library have been examined. 257 iv. Return the subset of models that produces the best per-258 formance on the validation set. 259 The forward model selection strategy is fast and efficient 260 but occasionally overfits, leading to poor ensemble perfor-261 mance. Hence, several ensemble selection strategies have 262 been recently proposed; for example, Sun and Pfahringer [61] 263 developed the bagging ensemble selection, which combines 264 bagging and ensemble selection. Furthermore, the ensemble 265 selection strategies can be divided into static and dynamic 266 methods. The static strategy selects a single subset of base 267 models during model training and applies it to predict all 268 the unseen instances. The static selection methods can be 269 grouped into ordering-based techniques and optimization-270 based techniques. 271 As the name implies, ordering-based techniques attempt to 272 order the base models with respect to specific criteria, and 273 only the top models are chosen as the optimal subset. Some 274 criteria for ordering the base models include validation error 275 and kappa measure [59]. Guo et al. [62] recently developed 276 a method to order the base models via an evaluation metric 277 that takes the margin and diversity into consideration. Mean-278 while, optimization-based techniques formulate the selec-279 tion process as an optimization problem that can be solved 280 using mathematical programming or heuristic optimization. 281 Static methods limits the flexibility of the ensemble selection process. 843 the study employed the SMOTE Tomek link (SMOTETomek) 844 to create new datasets with even class distribution since 845 all four datasets are imbalanced. Secondly, the study uses 846 the isolation forest (iForest) algorithm [135] to detect and 847 remove outliers in the datasets. Meanwhile, the iForest is an 848 ensemble implementation that creates isolation trees using 849 the given dataset. The isolation trees are repeatedly devel-850 oped by splitting the training set until all the samples are 851 isolated or the specified tree height is obtained. The exper-852 imental results show that the proposed approach achieved 853 superior classification performance than other methods and 854 prior research works. The proposed ensemble obtained 855 96.7%, 85.8%, 75.8%, and 100% accuracy when predicting 856 type 2 diabetes, hypertension, prehypertension, and CKD, 857 respectively. 858 Kazemi and Mirroshandel [136] proposed a novel ensem-859 ble method for the early detection of kidney stones, a common 860 disease affecting people globally. The study employed several 861 ML algorithms, including decision trees, naïve Bayes, and 862 artificial neural networks (ANN), to learn the relationships 863 between some biological features related to kidney stone 864 disease. Furthermore, the study developed a novel method 865 to combine the different classifiers. The combination method 866 involved assigning weights to the different classifiers via a 867 genetic algorithm (GA) based computation. Meanwhile, the 868 data used in the research was obtained between 2012 and 869 2016 from 936 patients with kidney stone disease. The pro-870 posed ensemble achieved a classification accuracy of 97.1%.</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 Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects","attachmentId":95562317,"attachmentType":"pdf","work_url":"https://www.academia.edu/92594515/A_Survey_of_Ensemble_Learning_Concepts_Algorithms_Applications_and_Prospects","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/92594515/A_Survey_of_Ensemble_Learning_Concepts_Algorithms_Applications_and_Prospects"><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="101002877" 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/101002877/Supervised_Ensemble_Learning_Comparative_Analysis_of_Algorithms_And_Methods">Supervised Ensemble Learning: Comparative Analysis of Algorithms And Methods</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="204969966" href="https://independent.academia.edu/RiccioDonato">Donato Riccio</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Ensemble techniques are often regarded as the cutting-edge answer to many machine learning problems. By training many models and aggregating their predictions, such strategies increase the predictive performance of a single model. This thesis discusses supervised ensemble learning, examines and compares classic and new ensemble learning approaches, and explores current difficulties and developments in the area.</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":"Supervised Ensemble Learning: Comparative Analysis of Algorithms And Methods","attachmentId":101663020,"attachmentType":"pdf","work_url":"https://www.academia.edu/101002877/Supervised_Ensemble_Learning_Comparative_Analysis_of_Algorithms_And_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-wsj-grid-card-view-pdf" href="https://www.academia.edu/101002877/Supervised_Ensemble_Learning_Comparative_Analysis_of_Algorithms_And_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-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="96216840" 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/96216840/Ensemble_Learning_Techniques_and_Applications_in_Pattern_Classification">Ensemble Learning Techniques and Applications in Pattern Classification</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="41985617" href="https://independent.academia.edu/AlanLiew1">Alan Liew</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2018</p><p class="ds-related-work--abstract ds2-5-body-sm">It is widely known that the best classifier for a given problem is often problem dependent and there is no one classification algorithm that is the best for all classification tasks. A natural question that arise is: can we combine multiple classification algorithms to achieve higher classification accuracy than a single one? That is the idea behind a class of methods called ensemble method. Ensemble method is defined as the combination of several classifiers with the aim of achieving lower classification error rate than using a single classifier. Ensemble methods have been applying to various applications ranging from computer aided medical diagnosis, computer vision, software engineering, to information retrieval. In this study, we focus on heterogeneous ensemble methods in which a fixed set of diverse learning algorithms are learned on the same training set to generate the different classifiers and the class prediction is then made based on the output of these classifiers (called...</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":"Ensemble Learning Techniques and Applications in Pattern Classification","attachmentId":98176672,"attachmentType":"pdf","work_url":"https://www.academia.edu/96216840/Ensemble_Learning_Techniques_and_Applications_in_Pattern_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-wsj-grid-card-view-pdf" href="https://www.academia.edu/96216840/Ensemble_Learning_Techniques_and_Applications_in_Pattern_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-wsj-grid-card" data-collection-position="6" 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="7" data-entity-id="29969703" 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/29969703/Empirical_Studies_and_Analysis_of_Ensemble_Learning_Techniques_in_Data_Mining">Empirical Studies and Analysis of Ensemble Learning Techniques in Data Mining</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="34217662" href="https://technoscienceacademy.academia.edu/IJSRSET">International Journal of Scientific Research in Science, Engineering and Technology IJSRSET</a></div><p class="ds-related-work--abstract ds2-5-body-sm">This Classification using ensemble generally combines multiple classifiers that results in the improvement in the accuracy of the classification. Experimenting with the same dataset using the single classifier provides lesser accuracy than ensemble techniques. Many researches have been carried out using the technique of combining the predictions of multiple classifiers to generate a single classifier. The produced classifiers provide more accurate results than any individual classifier. This paper focuses on the ability of ensemble techniques to improve the accuracy of basic J48 algorithm. Ensemble techniques like Bagging and Boosting improved the efficiency of the J48 classifier. Experiments have been carried out on many datasets taken from UCI repository to investigate the effects of ensemble techniques on J48 and Naïve Bayes algorithm. WEKA tool is used to measure the effectiveness of a classifier model.</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 Studies and Analysis of Ensemble Learning Techniques in Data Mining","attachmentId":50428590,"attachmentType":"pdf","work_url":"https://www.academia.edu/29969703/Empirical_Studies_and_Analysis_of_Ensemble_Learning_Techniques_in_Data_Mining","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/29969703/Empirical_Studies_and_Analysis_of_Ensemble_Learning_Techniques_in_Data_Mining"><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="20369491" 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/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-wsj-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><p class="ds-related-work--abstract ds2-5-body-sm">Data Mining is the study to get the knowledge from the huge data sources. It is a technology with huge potential to help the corporate ventures focus on the most important information in their data warehouses or database, so that it will help in making business decisions. Decision making with data mining is very much complex task. Ensemble technique is one of the common strategies to improve the accuracy of classifier. In general ensemble learning is an effective technology that combines the predictions from multiple base classifiers. Most commonly used ensemble techniques are Bagging and Boosting. Stacking is also one of the techniques, but it is less widely used. In this paper, we are focusing on bagging technique. An experiment is carried out using bagging with different datasets from UCI repository to study the classification accuracy improvement</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 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 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/20369491/Improving_Classification_Accuracy_through_ensemble_technique_in_Data_Mining"><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="5025587" 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/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-wsj-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><p class="ds-related-work--abstract ds2-5-body-sm">Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -from investment timing to drug discovery, and fraud detection to recommendation systems -where predictive accuracy is more vital than model interpretability.</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":"Ensemble Methods in Data Mining Improving Accuracy Through Combining Predictions BOOK","attachmentId":32258402,"attachmentType":"pdf","work_url":"https://www.academia.edu/5025587/Ensemble_Methods_in_Data_Mining_Improving_Accuracy_Through_Combining_Predictions_BOOK","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/5025587/Ensemble_Methods_in_Data_Mining_Improving_Accuracy_Through_Combining_Predictions_BOOK"><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":60459918,"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":60459918,"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_60459918" 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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class="ds-related-work--metadata ds2-5-body-xs">IRJET, 2021</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":"IRJET-Introduction to Ensemble Methods for Machine Learning Applications","attachmentId":73376022,"attachmentType":"pdf","work_url":"https://www.academia.edu/59469283/IRJET_Introduction_to_Ensemble_Methods_for_Machine_Learning_Applications","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/59469283/IRJET_Introduction_to_Ensemble_Methods_for_Machine_Learning_Applications"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" 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Applications","attachmentId":82718861,"attachmentType":"pdf","work_url":"https://www.academia.edu/74637701/Current_Issues_in_Ensemble_Methods_and_Its_Applications","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/74637701/Current_Issues_in_Ensemble_Methods_and_Its_Applications"><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="5" 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="6" data-entity-id="4403890" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title 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Homogeneous-Heterogeneous Ensemble of Classifiers</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="267356802" href="https://independent.academia.edu/ThiThanhThaoNGUYEN8">Thi Thanh Thao NGUYEN</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Communications in Computer and Information Science, 2020</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 Homogeneous-Heterogeneous Ensemble of Classifiers","attachmentId":101545236,"attachmentType":"pdf","work_url":"https://www.academia.edu/100837178/A_Homogeneous_Heterogeneous_Ensemble_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 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