Block matrix incremental feature selection method based on fuzzy rough minimum classification error

Block matrix incremental feature selection method based on fuzzy rough minimum classification error | 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 Block matrix incremental feature selection method based on fuzzy rough minimum classification error First Zhanwei Chen, Second Minggang Xing, Third Xu Li, Fourth Juan Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6104303/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 Inner product dependence, as an important feature evaluation function in fuzzy rough set theory, has the advantage of maintaining the maximum membership degree of samples to decisions while effectively characterizing classification errors. However, it is noteworthy that this method has limitations in describing classification errors: it can only estimate errors through partial samples and cannot achieve a more accurate error description for the overall sample set. To address this issue, this study constructs an inner product dependence function that can comprehensively represent the characteristics of the overall sample from the perspective of the domain and analyzes its relevant properties in depth. By leveraging matrix operations, this study design a feature selection algorithm based on the minimum classification error criterion (MCEFS). Furthermore, to cope with the dynamic changes in data environments, this study proposes an incremental feature selection algorithm based on block matrices (BM-MCEFS) by exploring the theory and methodological analysis of fuzzy rough sets in conjunction with incremental techniques. Finally, through a series of comparative experiments conducted on 10 public datasets, this paper fully verifies the feasibility of the proposed static algorithm and the effectiveness of the incremental algorithm. Feature selection Block matrix Incremental mechanisms Fuzzy decision Fuzzy rough set Full Text Additional Declarations No competing interests reported. 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. 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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-6104303","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448885921,"identity":"667f41c6-2730-4c3e-a86b-0050ccceb8f0","order_by":0,"name":"First Zhanwei Chen","email":"","orcid":"","institution":"Xinjiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"First","middleName":"Zhanwei","lastName":"Chen","suffix":""},{"id":448885922,"identity":"e9155af1-f14f-44fa-ba75-67cd68cc3a91","order_by":1,"name":"Second Minggang Xing","email":"","orcid":"","institution":"Xinjiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Second","middleName":"Minggang","lastName":"Xing","suffix":""},{"id":448885923,"identity":"175fb332-7450-46c1-bdd1-5779bb73aebc","order_by":2,"name":"Third Xu Li","email":"","orcid":"","institution":"Xinjiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Third","middleName":"Xu","lastName":"Li","suffix":""},{"id":448885924,"identity":"f6bf718f-dd0a-413e-8e80-07d4cb99da9e","order_by":3,"name":"Fourth Juan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBAC+/b+hw8+GNTUN7Y3EKnFgOcMs+GMgmOMzT0HiNUikcMmzfOBmbF9RgKRWswlcg8b8xiwMfPOfLzxBkONTTRBLZY97xIfzjGQYZOcnVZswXAsLbeBoJ7jCcYGbwzYeAxn55hJMDYcJkLLgQQzCR4DZgn7m2eI1GJwIsdMEqjFgHEGD5FaJHuOJRvOMDiWwNgD9EsCMX7hZ28++ODDn5oExvbDG298qLEhwi/IjpRIIEU5RAupOkbBKBgFo2BkAAA+eUIB5rr9bAAAAABJRU5ErkJggg==","orcid":"","institution":"Xinjiang Normal University","correspondingAuthor":true,"prefix":"","firstName":"Fourth","middleName":"Juan","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-02-25 10:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6104303/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6104303/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81651280,"identity":"872a28be-8a99-4afc-bdb7-bed00f85dfe4","added_by":"auto","created_at":"2025-04-29 16:16:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":948968,"visible":true,"origin":"","legend":"","description":"","filename":"Blockmatrixincrementalfeatureselectionmethodbasedonfuzzyroughminimumclassificationerror.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6104303/v1_covered_6ec7b158-395c-43fa-8ee6-9ec9e5b90ac0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Block matrix incremental feature selection method based on fuzzy rough minimum classification error","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":"Feature selection, Block matrix, Incremental mechanisms, Fuzzy decision, Fuzzy rough set","lastPublishedDoi":"10.21203/rs.3.rs-6104303/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6104303/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Inner product dependence, as an important feature evaluation function in fuzzy rough set theory, has the advantage of maintaining the maximum membership degree of samples to decisions while effectively characterizing classification errors. 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