On the (In)Significance of Feature Selection in High-Dimensional Datasets

On the (In)Significance of Feature Selection in High-Dimensional Datasets | 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 Article On the (In)Significance of Feature Selection in High-Dimensional Datasets Bhavesh Neekhra, Debayan Gupta, Partha Chakrabarti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7192153/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 Extensive research has been done on feature selection (FS) algorithms for high-dimensional datasets aiming to improve model performance, reduce computational cost and identify features of interest. We test the null hypothesis of using randomly selected features to compare against features selected by FS algorithms to validate the performance of the latter. Our results show that FS on high-dimensional datasets (in particular gene expression) in classification tasks is not useful. We find that (1) models trained on small subsets (0.02%-1% of all features) of randomly selected features almost always perform comparably to those trained on all features, and (2) a “typical”-sized random subset provides comparable or superior performance to that of top-k features selected in various published studies. Thus, our work challenges many feature selection results on high dimensional datasets, particularly in computational genomics. It raises serious concerns about studies that propose drug design or targeted interventions based on computationally selected genes, without further validation in a wet lab. Biological sciences/Computational biology and bioinformatics/Machine learning Scientific community and society/Scientific community/Research data Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementary.pdf Supplementary figure for the paper titled On the (In)Significance of Feature Selection in High-Dimensional Datasets 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-7192153","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":489942326,"identity":"1492fd65-d50e-4f74-9bf3-2a874ca719d7","order_by":0,"name":"Bhavesh Neekhra","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-5468-0812","institution":"Ashoka University","correspondingAuthor":true,"prefix":"","firstName":"Bhavesh","middleName":"","lastName":"Neekhra","suffix":""},{"id":489942327,"identity":"afcd3ce6-a32b-4122-98c2-ece4a3f162d7","order_by":1,"name":"Debayan Gupta","email":"","orcid":"","institution":"Ashoka University","correspondingAuthor":false,"prefix":"","firstName":"Debayan","middleName":"","lastName":"Gupta","suffix":""},{"id":489942328,"identity":"e351e646-c6cf-4e03-8d69-7286b3f13837","order_by":2,"name":"Partha Chakrabarti","email":"","orcid":"","institution":"Indian Institute of Technology Kharagpur","correspondingAuthor":false,"prefix":"","firstName":"Partha","middleName":"","lastName":"Chakrabarti","suffix":""}],"badges":[],"createdAt":"2025-07-23 05:05:07","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7192153/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7192153/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88926682,"identity":"b38fea6f-5b1f-44d2-b4a3-7407d194d948","added_by":"auto","created_at":"2025-08-12 19:19:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1382788,"visible":true,"origin":"","legend":"Article File","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7192153/v1_covered_f234e163-6cb4-4c1e-b17b-4ee7250acd18.pdf"},{"id":88333880,"identity":"11388bc5-a7f1-48b9-9386-e8475345565c","added_by":"auto","created_at":"2025-08-05 11:35:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4898258,"visible":true,"origin":"","legend":"Supplementary figure for the paper titled On the (In)Significance of Feature Selection in High-Dimensional Datasets","description":"","filename":"Supplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7192153/v1/dfbfaa312f1fcc9336e2d879.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"On the (In)Significance of Feature Selection in High-Dimensional Datasets","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-7192153/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7192153/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Extensive research has been done on feature selection (FS) algorithms for high-dimensional datasets aiming to improve model performance, reduce computational cost and identify features of interest. 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