FWB-SMOTE: Feature-Weighted Borderline Synthetic Minority Oversampling Technique for Class Imbalance Problems

FWB-SMOTE: Feature-Weighted Borderline Synthetic Minority Oversampling Technique for Class Imbalance Problems | 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 FWB-SMOTE: Feature-Weighted Borderline Synthetic Minority Oversampling Technique for Class Imbalance Problems Mingyuan Liu, Qicheng Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7332833/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract SMOTE is a classic method for handling imbalanced datasets, but it has issues such as introducing noisy samples, blurring boundaries, and equalizing feature weights when generating minority class samples. To improve the quality of oversampling, this paper proposes the FWB-SMOTE (Feature-Weighted Borderline-SMOTE) algorithm. The algorithm first calculates the Euclidean distance matrix between minority class samples and the entire dataset, and filters out noise by combining the proportion of neighboring samples' categories. It then uses the feature splitting gain of XGBoost to quantify feature importance, normalizes the weights and maps them to feature dimensions, making key features dominate in sample generation and suppressing interference from redundant features. Finally, the k-nearest neighbor algorithm is used to determine the minority class boundary samples in the weighted feature space, and the selected boundary samples are oversampled by integrating feature weights. Comparative experiments on 44 groups of KEEL datasets verify that FWB-SMOTE algorithm significantly outperforms 8 comparison algorithms including SMOTE and Borderline-SMOTE in terms of AUC and G-mean indicators on DT, SVM, and KNN classifiers, confirming the effectiveness of this algorithm in improving the quality of minority class sample generation. Imbalanced learning Oversampling Class imbalance SMOTE Feature weighted Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Mar, 2026 Reviews received at journal 11 Jan, 2026 Reviews received at journal 10 Jan, 2026 Reviewers agreed at journal 06 Jan, 2026 Reviewers agreed at journal 29 Dec, 2025 Reviewers agreed at journal 29 Dec, 2025 Reviewers agreed at journal 29 Dec, 2025 Reviewers agreed at journal 29 Dec, 2025 Reviews received at journal 28 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers invited by journal 11 Sep, 2025 Editor assigned by journal 11 Sep, 2025 Submission checks completed at journal 10 Aug, 2025 First submitted to journal 09 Aug, 2025 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-7332833","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513483003,"identity":"7efff69c-aaf2-40a6-a9c0-22688adbb854","order_by":0,"name":"Mingyuan Liu","email":"","orcid":"","institution":"Yantai University","correspondingAuthor":false,"prefix":"","firstName":"Mingyuan","middleName":"","lastName":"Liu","suffix":""},{"id":513483005,"identity":"b847f9b3-114a-4063-ab77-8a5d01be8886","order_by":1,"name":"Qicheng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYBACPgYGxgcQZgKRWtgYGJgNIKpJ0MImQaIW9uRn1bw/DjPws+cYMPzcQYwWnmdmt3kSDjNI9rwxYOw9Q4wWiRw2sBaDGzkGzIxtRGopBmmxJ0kLM9gWCaK18DwzlpyTls4jceZZwcFeYrTwsyc//PDGxlqOvz1544OfxGgBRQcTDwMDD4h5gCgNIC2MP4hUOgpGwSgYBSMUAACR6i0RqhwHPAAAAABJRU5ErkJggg==","orcid":"","institution":"Yantai University","correspondingAuthor":true,"prefix":"","firstName":"Qicheng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-08-09 09:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7332833/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7332833/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91583146,"identity":"5aba9e1d-23d0-453d-8379-df63b1c70637","added_by":"auto","created_at":"2025-09-18 05:05:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":750188,"visible":true,"origin":"","legend":"","description":"","filename":"FWBSMOTE.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7332833/v1_covered_aa446483-16e7-47c0-b326-9d5b4808563f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"FWB-SMOTE: Feature-Weighted Borderline Synthetic Minority Oversampling Technique for Class Imbalance Problems","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Imbalanced learning, Oversampling, Class imbalance, SMOTE, Feature weighted","lastPublishedDoi":"10.21203/rs.3.rs-7332833/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7332833/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSMOTE is a classic method for handling imbalanced datasets, but it has issues such as introducing noisy samples, blurring boundaries, and equalizing feature weights when generating minority class samples. 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