Attribute reduction based on a rapid variable granular ball generation model

Attribute reduction based on a rapid variable granular ball generation model | 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 Attribute reduction based on a rapid variable granular ball generation model Ke Sun, Bing Huang, Tianxing Wang, Huaxiong Li, Xin Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4555419/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 Attribute reduction is a key step in processing large-scale datasets, where the Granular Ball Neighborhood Rough Set (GBNRS) can significantly enhance the performance of attribute reduction compared to the traditional Neighborhood Rough Set (NRS). However, the GBNRS algorithm faces such challenges as a sharp increase in computational costs in high-dimensional spaces. To address these issues, this study introduces a new granular ball quality index to judge the separability degree of decision classes, and on the basis of this index, a rapid variable granular ball generation model (RVGBGM) is proposed. Compared with GBNRS, RVGBGM has the following advantages: 1) it reduces the number of granular balls and can quickly reflect the separability degree of different decision classes with few granular balls, 2) it constructs granular balls by using label information and shortens the time of granular ball construction, and 3) it can adjust the radius of granular balls adaptively by using parameters to determine the optimal granular ball radius for different datasets. Finally, we compare the RVGBGM algorithm with classical attribute reduction algorithms and the current state-of-the-art granular ball algorithm on 11 datasets. The proposed algorithm significantly improves algorithm efficiency while maintaining high accuracy. Attribute reduction Granular ball generation Rapid variable granular ball generation model Separability degree 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. 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-4555419","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":318107409,"identity":"4f10fb91-7922-403a-926d-c92eaf726868","order_by":0,"name":"Ke Sun","email":"","orcid":"","institution":"Nanjing Audit University","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Sun","suffix":""},{"id":318107410,"identity":"7a2b9b1b-b839-4537-b722-3bafde9ac2e5","order_by":1,"name":"Bing Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACAwkeMJ3AxsDA+CChwoY0LcwGD86kkaAFiNkkH7YdIqzFXLr3mMTPHbV5fNLt1yoS2A4w8Ld3J+DVYjnnXJpk75njxWwyZ8puJPDcYZA4c3YDfofdyDGT4G07ltgmkZN2I0HiGdCpuYS1SP6FailIMDhMnBZp3rYaoJb0YwwJCcRouXPG2Fq27UAxm0QOs0TCgTQewn653WN4821bXZ78jPSHH3/+s5Hjb+/FrwUKDgMxjwGIxUOMchCoA2L2B8SqHgWjYBSMghEGANr0TO/+9pU5AAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing Audit University","correspondingAuthor":true,"prefix":"","firstName":"Bing","middleName":"","lastName":"Huang","suffix":""},{"id":318107411,"identity":"f221156a-2700-4bf8-84ab-25d0a73d05d8","order_by":2,"name":"Tianxing Wang","email":"","orcid":"","institution":"Nanjing Audit University","correspondingAuthor":false,"prefix":"","firstName":"Tianxing","middleName":"","lastName":"Wang","suffix":""},{"id":318107412,"identity":"9eebaccd-4e5e-4790-a207-9a9150e3b4d4","order_by":3,"name":"Huaxiong Li","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Huaxiong","middleName":"","lastName":"Li","suffix":""},{"id":318107413,"identity":"14f66128-e176-42d6-838c-eaa06cde8968","order_by":4,"name":"Xin Wang","email":"","orcid":"","institution":"Nanjing Audit University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-10 02:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4555419/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4555419/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59549348,"identity":"f5a146af-c4f8-4664-b25b-e64ee60ae821","added_by":"auto","created_at":"2024-07-03 06:00:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3151662,"visible":true,"origin":"","legend":"","description":"","filename":"20240607.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4555419/v1_covered_d414943b-758c-420e-a49b-5b5797139987.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Attribute reduction based on a rapid variable granular ball generation model","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":"Attribute reduction, Granular ball generation, Rapid variable granular ball generation model, Separability degree","lastPublishedDoi":"10.21203/rs.3.rs-4555419/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4555419/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Attribute reduction is a key step in processing large-scale datasets, where the Granular Ball Neighborhood Rough Set (GBNRS) can significantly enhance the performance of attribute reduction compared to the traditional Neighborhood Rough Set (NRS). 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