Fuzzy Feature Selection Using Fuzzy C-Means Clustering and Recursive Feature Elimination (FCM-RFE)

Fuzzy Feature Selection Using Fuzzy C-Means Clustering and Recursive Feature Elimination (FCM-RFE) | 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 Fuzzy Feature Selection Using Fuzzy C-Means Clustering and Recursive Feature Elimination (FCM-RFE) Phichsinee Khongja, Amit Kumar Saxena, Damodar Patel, Phumin Sumalai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7984249/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract In machine learning, feature selection is crucial for reducing computing costs, increasing generalization, reducing dimensionality, and improving model interpretability. Due to multicollinearity and redundancy, traditional approaches often encounter difficulties when dealing with high-dimensional data. We propose a hybrid framework called Fuzzy Feature Selection using Fuzzy C-Means Clustering and Recursive Feature Elimination (FCM-RFE), which combines fuzzy logic, filter, and wrapper approaches, to address these problems. In order to capture complex relationships, fuzzy C-Means clustering first partitions related features into soft clusters. Then, within each cluster, less significant features are repeatedly eliminated using Recursive Feature Elimination with Random Forest (RFE-RF). For more precise selection, features are ranked according to the strength of their cluster link using a fuzzy membership-based scoring system. Experiments on 18 benchmark datasets using KNN and SVM classifiers evaluated metrics including accuracy, precision, recall, F1-score, specificity, and AUC-ROC. The proposed approach maintained or enhanced performance while significantly decreasing dimensionality, selecting, on average, only 4.1% of the original features. The maximum accuracy was 92.75% for SVM with FCM-RFE and 89% for KNN. The proposed method demonstrated effectiveness and scalability for high-dimensional data analysis, outperforming eight state-of-the-art techniques and demonstrating computing efficiency. This framework is suitable for high-dimensional data analysis in various disciplines because it not only increases classification performance but also improves interpretability and scalability. Fuzzy C-Means Clustering Recursive Feature Elimination Fuzzy Feature Selection Dimensionality Reduction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Jan, 2026 Reviews received at journal 18 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviews received at journal 10 Dec, 2025 Reviewers agreed at journal 20 Nov, 2025 Reviewers invited by journal 03 Nov, 2025 Editor assigned by journal 31 Oct, 2025 Submission checks completed at journal 30 Oct, 2025 First submitted to journal 29 Oct, 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. 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-7984249","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":539094423,"identity":"a08e7d3b-2d3f-440d-9598-4e89cc72f924","order_by":0,"name":"Phichsinee Khongja","email":"","orcid":"","institution":"Khon Kaen University","correspondingAuthor":false,"prefix":"","firstName":"Phichsinee","middleName":"","lastName":"Khongja","suffix":""},{"id":539094424,"identity":"fab32690-b140-4f9e-941b-99ee65a38168","order_by":1,"name":"Amit Kumar Saxena","email":"","orcid":"","institution":"Guru Ghasidas 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(FCM-RFE)","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer 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Due to multicollinearity and redundancy, traditional approaches often encounter difficulties when dealing with high-dimensional data. We propose a hybrid framework called Fuzzy Feature Selection using Fuzzy C-Means Clustering and Recursive Feature Elimination (FCM-RFE), which combines fuzzy logic, filter, and wrapper approaches, to address these problems. In order to capture complex relationships, fuzzy C-Means clustering first partitions related features into soft clusters. Then, within each cluster, less significant features are repeatedly eliminated using Recursive Feature Elimination with Random Forest (RFE-RF). For more precise selection, features are ranked according to the strength of their cluster link using a fuzzy membership-based scoring system. Experiments on 18 benchmark datasets using KNN and SVM classifiers evaluated metrics including accuracy, precision, recall, F1-score, specificity, and AUC-ROC. 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