Simulation comparison of the effects of missing data imputation methods on classification performance in high dimensional data

Simulation comparison of the effects of missing data imputation methods on classification performance in high dimensional data | 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 Simulation comparison of the effects of missing data imputation methods on classification performance in high dimensional data Bugra VAROL, Imran Kurt Omurlu, Mevlut Ture This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5407580/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 The study aims to examine the performance of different missing data imputation methods on accurately estimating missing data in high dimensional datasets and their impact on classification using extreme learning machines (ELM). Random datasets were generated with n = 150 observations, p = 500 independent variables, and different missing data rates. Various imputation methods were used, including mean, median, random, k-nearest neighbors (KNN), missing value imputation with random forests (I-RF), multivariate imputations by chained equations with classification and regression trees (MICE-CART), as well as direct and indirect use of regularized regression (DURR and IURR) methods specifically developed for high dimensional data. The performance of the methods was evaluated based on their proximity to the reference classification scores obtained using ELM. I-RF, MICE-CART, DURR, and IURR, followed by KNN methods, exhibited better performance at low missing rates, while DURR and IURR methods stood out at high missing rates. Extreme learning machine High dimensional data Imputation Classification Simulation 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5407580","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375333366,"identity":"1efd8045-52ea-4ede-9282-140eef39a66d","order_by":0,"name":"Bugra VAROL","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACAziLnfn4jw9Amo2daC3MbAmSM0BamInXwmMgzQNmENBiLpF+7cPPNrt8fqAtxja/tsnzMTMwfviYg1uL5Yyc4pm9bcmWM5uZDyTn9t02bGNmYJacuQ2Pw27kJDPwnGE2MDjMlnA4t+c2I1ALGzMvAS2Mf87UA7XwGDZb9ty2J0JL+mFmnorDIC3GzAw/bicS1GLZ84aZWabiuIFkM1saY2/D7eQ2ZsZmvH4xZ09/zPjGoNqAn735GMOPP7dt57c3H/zwEY8WBgYeRNQwMLaByQZ86oGA/QES5w8BxaNgFIyCUTAiAQCpGkuLPsCy4gAAAABJRU5ErkJggg==","orcid":"","institution":"Adnan Menderes University","correspondingAuthor":true,"prefix":"","firstName":"Bugra","middleName":"","lastName":"VAROL","suffix":""},{"id":375333367,"identity":"3ead14fe-cef8-4795-b6f0-92f8bb9a5968","order_by":1,"name":"Imran Kurt Omurlu","email":"","orcid":"","institution":"Adnan Menderes University","correspondingAuthor":false,"prefix":"","firstName":"Imran","middleName":"Kurt","lastName":"Omurlu","suffix":""},{"id":375333368,"identity":"03278d36-d388-4f35-a635-6a972ea00dc3","order_by":2,"name":"Mevlut Ture","email":"","orcid":"","institution":"Adnan Menderes University","correspondingAuthor":false,"prefix":"","firstName":"Mevlut","middleName":"","lastName":"Ture","suffix":""}],"badges":[],"createdAt":"2024-11-07 07:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5407580/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5407580/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71462445,"identity":"ff41d7e1-d671-4583-878e-49f0a569865f","added_by":"auto","created_at":"2024-12-16 01:16:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1275717,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptJournalofStatisticalTheoryandApplicationsref.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5407580/v1_covered_50f823d8-2f11-43ad-8c0b-dd5b96a07074.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Simulation comparison of the effects of missing data imputation methods on classification performance in high dimensional data","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":"Extreme learning machine, High dimensional data, Imputation, Classification, Simulation","lastPublishedDoi":"10.21203/rs.3.rs-5407580/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5407580/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study aims to examine the performance of different missing data imputation methods on accurately estimating missing data in high dimensional datasets and their impact on classification using extreme learning machines (ELM). 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