BMFS: Bidirectional weighted approach for multi-label feature selection algorithm

BMFS: Bidirectional weighted approach for multi-label feature selection algorithm | 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 Article BMFS: Bidirectional weighted approach for multi-label feature selection algorithm Weiguo Yi, Bin Ma, LingWei Yan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4127126/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 Shortcomings of the existing multi-label feature selection algorithms, such as non-considering the correlation of label space, ignoring the possible difference of label importance in feature space, cause the selected features to not accurately and comprehensively describe the sample, and make the calculation process complex. To solve these problems, this paper proposes an improved affinity propagation (AP) clustering method. Primarily in this method, the label space is divided according to the label correlation, and the label importance measurement and feature weight are also introduced to simplify the calculation. A multi-label rough membership function is proposed based on the definitions of the multi-label classification margin and the adaptive neighborhood radius and the neighborhood class , and then the multi-label fuzzy neighborhood rough set model is constructed by mapping the rough set to the fuzzy set, in which the fuzzy neighborhood entropy and multi-label fuzzy neighborhood entropy are defined. Finally, the bidirectional weighted multi-label feature selection algorithm is proposed based on fuzzy neighborhood entropy(BMFS). The experimental results on 10 multi-label datasets under Multi-label K-Nearest Neighbor (MLKNN) classifier show effectiveness and feasibility of this algorithm. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Scientific data Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile.pdf 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4127126","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":286873053,"identity":"81c824d1-71aa-4487-ba08-ef132fed78ab","order_by":0,"name":"Weiguo Yi","email":"","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Weiguo","middleName":"","lastName":"Yi","suffix":""},{"id":286873055,"identity":"b6ac12d7-1ea4-49ba-8e15-5752fa515755","order_by":1,"name":"Bin Ma","email":"","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Ma","suffix":""},{"id":286873057,"identity":"78f381a9-2818-41d9-aa65-803ede63fb29","order_by":2,"name":"LingWei Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYHACxgMJIIqZ/+ODhAoJOXli9EC1MBgbPDhjYWzYQIwWKG0m+bCtIhHOxQUMjp89cODhjtrEfnaGNInEeRIJjA3MDx/dwKflTF7CgcQzxxNnNjMctkjcJpHHzsBmbJyDT8uBHIMDiW3HcjccZmy8AdRSzNjAwyaNV8v5NxAt+w8zM0gkzpFIbDhASMsNsC01uRuY2ZiA6onQInkDbMuB+hmHeZgNEo5JGBs2E/AL3/kcw4c/2+qM+fvPMD78UVMnJ8/e/PAxPi0KB8DUYSQhZjzKQUC+AUzVEVA2CkbBKBgFIxoAADHMVX0SnXlLAAAAAElFTkSuQmCC","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"LingWei","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2024-03-19 04:14:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4127126/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4127126/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57459265,"identity":"44f65815-4676-4c12-9741-020bccc06b1d","added_by":"auto","created_at":"2024-05-31 02:29:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":829503,"visible":true,"origin":"","legend":"","description":"","filename":"TemplateforsubmissionstoScientificReports4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4127126/v1_covered_4772dd1c-edde-47e2-bcfb-179b8d7f0395.pdf"},{"id":54259283,"identity":"4f6b30f7-ba95-4c5e-957d-d847760b0a2a","added_by":"auto","created_at":"2024-04-08 02:32:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":305390,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4127126/v1/ea0f92a8e2ab29309467c299.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"BMFS: Bidirectional weighted approach for multi-label feature selection algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4127126/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4127126/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Shortcomings of the existing multi-label feature selection algorithms, such as non-considering the correlation of label space, ignoring the possible difference of label importance in feature space, cause the selected features to not accurately and comprehensively describe the sample, and make the calculation process complex. 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