Multi-Criteria Decision Making Methods for Ensemble Feature Selection using q-Rung Orthopair Hesitant Fuzzy Distance and Similarity Measures

Multi-Criteria Decision Making Methods for Ensemble Feature Selection using q-Rung Orthopair Hesitant Fuzzy Distance and Similarity Measures | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-Criteria Decision Making Methods for Ensemble Feature Selection using q-Rung Orthopair Hesitant Fuzzy Distance and Similarity Measures Janani Kesavan, Kavitha Seethapathy, Satheeshkumar J, Rakkiyappan Rajan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1583632/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper deals with ensemble feature selection using the q-rung orthopair hesitant fuzzy multi-criteria decision-making (MCDM) methods including VIse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Combinative distance-based assessment (CODAS). The novalty of this paper is to design the three MCDM algorithms based on q-rung orthopair hesitant fuzzy sets with different distance and similarity measures. The well known distance and similarity measures are to be taken such as Hausdorff measure, hybrid Hausdorff and distance measure, synergetic measure, similarity for Hausdorff measure, similarity for hybrid Hausdorff and distance measure, similarity for synergetic measure, ordered Hausdorff measure, ordered hybrid Hausdorff and distance measure, similarity for ordered Hausdorff measure and similarity for ordered hybrid Hausdorff and distance measure This is the first time in the literature, an ensemble feature selection problem is modeled as a q-rung orthopair hesitant fuzzy MCDM extended to VIKOR, TOPSIS and CODAS techniques with distance and similarity measures. By using q-ROHFS VIKOR, TOPSIS and CODAS methods, a score is assigned to each feature based on the values of the preference matrix. At last, an output rank vector is produced for all features, from which the user can select the desired number of features. To prove the efficiency and optimality of our proposed method, we compared with the basic filter-based feature selections and ensemble feature selection by using feature ranking strategy. Our method is superior and efficient than the ensemble methods based on the accuracy and F-score levels. Distance and Similarity Measures Ensemble Feature Selection CODAS TOPSIS VIKOR Machine Learning Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1583632","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":118004990,"identity":"8fdd5b75-07b7-4aae-9804-7fb32a3d0b44","order_by":0,"name":"Janani Kesavan","email":"","orcid":"","institution":"Bharathiar University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Janani","middleName":"","lastName":"Kesavan","suffix":""},{"id":118004991,"identity":"081998b3-72f3-49f5-a816-c5161ac2218b","order_by":1,"name":"Kavitha Seethapathy","email":"","orcid":"","institution":"Bharathiar 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08:44:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1583632/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1583632/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":23549421,"identity":"5d8e6dda-98d8-41e0-b59e-0f6f627da00e","added_by":"auto","created_at":"2022-07-06 18:21:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3282942,"visible":true,"origin":"","legend":"","description":"","filename":"manuscirptSoftcomp.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1583632/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"Multi-Criteria Decision Making Methods for Ensemble Feature Selection using q-Rung Orthopair Hesitant Fuzzy Distance and Similarity Measures","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1583632/latest.pdf' target='_blank'\u003edownload as a 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Learning","lastPublishedDoi":"10.21203/rs.3.rs-1583632/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1583632/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper deals with ensemble feature selection using the q-rung orthopair hesitant fuzzy multi-criteria decision-making (MCDM) methods including VIse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Combinative distance-based assessment (CODAS). The novalty of this paper is to design the three MCDM algorithms based on q-rung orthopair hesitant fuzzy sets with different distance and similarity measures. The well known distance and similarity measures are to be taken such as Hausdorff measure, hybrid Hausdorff and distance measure, synergetic measure, similarity for Hausdorff measure, similarity for hybrid Hausdorff and distance measure, similarity for synergetic measure, ordered Hausdorff measure, ordered hybrid Hausdorff and distance measure, similarity for ordered Hausdorff measure and similarity for ordered hybrid Hausdorff and distance measure This is the first time in the literature, an ensemble feature selection problem is modeled as a q-rung orthopair hesitant fuzzy MCDM extended to VIKOR, TOPSIS and CODAS techniques with distance and similarity measures. By using q-ROHFS VIKOR, TOPSIS and CODAS methods, a score is assigned to each feature based on the values of the preference matrix. At last, an output rank vector is produced for all features, from which the user can select the desired number of features. To prove the efficiency and optimality of our proposed method, we compared with the basic filter-based feature selections and ensemble feature selection by using feature ranking strategy. Our method is superior and efficient than the ensemble methods based on the accuracy and F-score levels.","manuscriptTitle":"Multi-Criteria Decision Making Methods for Ensemble Feature Selection using q-Rung Orthopair Hesitant Fuzzy Distance and Similarity Measures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-06 18:20:54","doi":"10.21203/rs.3.rs-1583632/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8b1a749a-f78b-431b-a853-b62cac0f158b","owner":[],"postedDate":"July 6th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-16T20:09:28+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-06 18:20:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1583632","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1583632","identity":"rs-1583632","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}