Attribute Reduction Method Based on Binary Sparrow Search Algorithm and Neighborhood Rough Set

Attribute Reduction Method Based on Binary Sparrow Search Algorithm and Neighborhood Rough Set | 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 Method Based on Binary Sparrow Search Algorithm and Neighborhood Rough Set Meixuan Li, Wei Liu, Chun Yan, Mengchao Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2071258/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 In this paper, an attribute reduction algorithm of neighborhood rough set based on variable precision is proposed, and the lower approximate calculation formula is redefined, which solves the shortcomings of traditional neighborhood rough set, such as fault tolerance and weak noise resistance. Considering that the traditional attribute reduction algorithm is serial search, it is easy to fall into the local optimal solution. Therefore, this paper proposes to combine intelligent optimization algorithm with neighborhood rough set. On the basis of the excellent performance of continuous sparrow search algorithm (SSA), a binary sparrow algorithm (BSSA) is proposed to solve the binary discrete problem, which is used to solve the optimal combination of attributes. In this paper, according to the definition of positive region monotonicity, distance matrix is introduced. For the positive region sample in a single attribute, it can be omitted in the subsequent calculation, and the calculation of positive region is transformed into the form of adding several matrices, which provides an effective method to solve positive region for variable precision neighborhood rough set. Finally, five UCI standard data sets were used as experimental objects to compare with other 4 attribute reduction algorithms. The experimental results show that both the number of attribute parsimony and the accuracy of classification, the proposed algorithm shows a strong advantage. Sparrow search algorithm Neighborhood rough set Attribute Reduction 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. 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-2071258","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":151717291,"identity":"d0bcc431-9f5b-454e-92c3-949223d8b810","order_by":0,"name":"Meixuan Li","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meixuan","middleName":"","lastName":"Li","suffix":""},{"id":151717292,"identity":"7ccb4b15-d0f3-481a-b65b-15f6edb40156","order_by":1,"name":"Wei Liu","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Liu","suffix":""},{"id":151717293,"identity":"b90cbfcd-81aa-4067-8578-fa7424f361bb","order_by":2,"name":"Chun Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYJACZhBhwMDA+OBDhY0cG3v7AaK1MBvOOJNmzMdzJoFoLWzSvG2HEudJOBjgVW5w/OzBz4Vtd+TNJXKPSc5sO5DeJsGQwPCjYhtuLWfykqVntj0z3DkjL9niw7k7uW3SjQcYe87cxq3lQI4ZM2/b4QSDGzmGN2eUPcttkzmQwMzYhkfL+TdwLQbSPGyH09kkEgzwa7mBsMVImgfIIKhF8sYbY2mec4cNN5x5YwwKZMM2YCAfxOcXvvM5hp95yg7LGxzPMQRFpbx8e/vBBz8qcGtROIBNFKsgDMg34JMdBaNgFIyCUQACAKXRXwCEUKcgAAAAAElFTkSuQmCC","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chun","middleName":"","lastName":"Yan","suffix":""},{"id":151717294,"identity":"3f786a19-7187-46eb-82b0-870fc9d1412a","order_by":3,"name":"Mengchao Zhang","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mengchao","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2022-09-16 06:20:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2071258/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2071258/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":43557321,"identity":"b74bc631-a545-482e-a91e-90a4244f216a","added_by":"auto","created_at":"2023-09-22 20:55:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":571729,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2071258/v1_covered_e1cbef49-2b68-4a06-9e5b-7363cc3827bf.pdf"}],"financialInterests":"","formattedTitle":"Attribute Reduction Method Based on Binary Sparrow Search Algorithm and Neighborhood Rough Set","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":true,"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":"Sparrow search algorithm, Neighborhood rough set, Attribute Reduction","lastPublishedDoi":"10.21203/rs.3.rs-2071258/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2071258/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In this paper, an attribute reduction algorithm of neighborhood rough set based on variable precision is proposed, and the lower approximate calculation formula is redefined, which solves the shortcomings of traditional neighborhood rough set, such as fault tolerance and weak noise resistance. Considering that the traditional attribute reduction algorithm is serial search, it is easy to fall into the local optimal solution. Therefore, this paper proposes to combine intelligent optimization algorithm with neighborhood rough set. On the basis of the excellent performance of continuous sparrow search algorithm (SSA), a binary sparrow algorithm (BSSA) is proposed to solve the binary discrete problem, which is used to solve the optimal combination of attributes. In this paper, according to the definition of positive region monotonicity, distance matrix is introduced. For the positive region sample in a single attribute, it can be omitted in the subsequent calculation, and the calculation of positive region is transformed into the form of adding several matrices, which provides an effective method to solve positive region for variable precision neighborhood rough set. Finally, five UCI standard data sets were used as experimental objects to compare with other 4 attribute reduction algorithms. 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