LMSE-ILSO: a variable selection method considering information loss without dimension limitation

LMSE-ILSO: a variable selection method considering information loss without dimension limitation | 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 Short Report LMSE-ILSO: a variable selection method considering information loss without dimension limitation Shaogang Chen, Qinmei Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6651429/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 the era of big data, the effective screening of massive data has become an important issue, and feature selection methods have emerged. Filtered, wrapped, and embedded methods have been applied to feature selection. However, these methods do not consider the information loss of unselected features in the feature selection process, and the number of variables that can be selected by some methods cannot be greater than the sample size. Therefore, this study introduces Shapley value to describe the information loss of unselected features, defines the loss term as the sum of mean square error and information loss of unselected features, and proposes LMSE-ILSO method. The empirical performance of LMSE-ILSO is verified by numerical simulation: The LMSE-ILSO method is equivalent to Adaptive Lasso in low-dimensional cases and equivalent to SCAD in high-dimensional cases; The number of feature selection of LMSE-ILSO method is not limited by the sample size; When the loss of unselected feature information is consistent with the proportion of the coefficient, the LMSE-ILSO method can accurately identify and estimate the coefficient by adjusting the parameters. feature selection information loss Shapley value 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. 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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-6651429","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":497342863,"identity":"ec179e3d-69fb-4362-9081-a2bdd6a4feb8","order_by":0,"name":"Shaogang Chen","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Shaogang","middleName":"","lastName":"Chen","suffix":""},{"id":497342864,"identity":"c9045a1b-f1e6-4c16-915b-78693873a811","order_by":1,"name":"Qinmei Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACAyA+AMQ8/MzMBw58+EGKFsn2tsSDM3uI1AJhnDljfJiDjQgt5hLJGw8X5tjIMNzI+XCYgYdBnl/sAH4tljPSCg7P3JbGwzgjd8PhAgsGw5mzEwg47EaOwWHebYd5mCWAWmbwMCQY3CZOy38eNomcB4d52IjXcoCHh+cMA5FazjwrAGpJ5pFgbzMABrIEEX45nrz5M+82O3v7w8yPP3z4YSPPL01ACwNS1ICABEHlGFpGwSgYBaNgFGACAOYJR6pBf0BHAAAAAElFTkSuQmCC","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":true,"prefix":"","firstName":"Qinmei","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2025-05-13 04:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6651429/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6651429/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100941263,"identity":"1cac63a5-f4c0-45cc-bc09-ab8ade6f27d7","added_by":"auto","created_at":"2026-01-23 04:40:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1441552,"visible":true,"origin":"","legend":"","description":"","filename":"paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6651429/v1_covered_43adec8b-ce6f-429f-9716-85fbfeedf4f9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"LMSE-ILSO: a variable selection method considering information loss without dimension limitation","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":"feature selection, information loss, Shapley value","lastPublishedDoi":"10.21203/rs.3.rs-6651429/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6651429/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the era of big data, the effective screening of massive data has become an important issue, and feature selection methods have emerged. 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