A Robust Classifier for Label Noise Using Random Forest Kernel

A Robust Classifier for Label Noise Using Random Forest Kernel | 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 A Robust Classifier for Label Noise Using Random Forest Kernel Shihab Shahriar Khan, Ahmedul Kabir, Muhammad Ibrahim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7849525/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 Many real-world classification datasets suffer from the presence of label noise that negatively impacts classification performance. A prominent group of label noise detection methods is nearest-neighbor-based, which tends to unfairly punish clean near-boundary samples. To alleviate this issue, we propose a new noise detection method that uses the distance function implicitly defined by randomized tree ensembles to find the nearest neighbors. These ensembles have been shown to be robust in the face of label noise, an important property that is exploited by the proposed detector method. In the first phase of our investigation, we analyze the characteristics and demonstrate the effectiveness of this noise detector. Next, we compare several ways of integrating the noise likelihood estimates obtained in the first phase with existing classification algorithms. Lastly, we propose a novel noise-robust variant of random forest that significantly outperforms regular random forest in the presence of high level of label noise and is competitive with robust state-of-the-art classification algorithms across a number of benchmark datasets. Artificial Intelligence and Machine Learning Random forest Noise filtering Robust machine learning Noisy data Full Text Additional Declarations The authors declare no competing interests. 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-7849525","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528864149,"identity":"2f74d32e-c81b-45ec-8f93-e6b989db79f4","order_by":0,"name":"Shihab Shahriar Khan","email":"","orcid":"","institution":"Department of Computer Science and Engineering, Michigan State University, Michigan, United States","correspondingAuthor":false,"prefix":"","firstName":"Shihab","middleName":"Shahriar","lastName":"Khan","suffix":""},{"id":528864150,"identity":"de13349a-828b-46a7-8701-c529b74f52dd","order_by":1,"name":"Ahmedul Kabir","email":"","orcid":"https://orcid.org/0000-0001-5983-6775","institution":"Institute of Information Technology, University of Dhaka, Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Ahmedul","middleName":"","lastName":"Kabir","suffix":""},{"id":528864151,"identity":"14cb75b1-5b20-4250-886f-5940f1eec4ed","order_by":2,"name":"Muhammad Ibrahim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACNgkGBmYGAwY7Nv7mAxJgIQkglxgtyXwSxxKI08IA1sLAwDiPIceAOC180r0PPxcUbGNmYzjz8TYPg508g3TzBvwOkzluLD3D4DYfG3PvZmsehmTDBpljBQT8ksYgzWNwG2jL2W3SPAzMCQwSOYS8n8b8G6iFsY0h5xlQSz1RWtikoVqADIbDRGiROcZmDdSSzCZxzNhyjsFxwzZCfpGf3cZ8m+fPbTv5/uaHN95UVMvzEwoxNAB0Ehsp6kfBKBgFo2AUYAcA8zg3XCHtGYEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3284-8535","institution":"Department of Computer Science and Engineering, University of Dhaka, Bangladesh","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Ibrahim","suffix":""}],"badges":[],"createdAt":"2025-10-13 13:23:32","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-7849525/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7849525/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93493795,"identity":"3bdcd8f7-4abb-4c70-b2c3-96fe7440eff2","added_by":"auto","created_at":"2025-10-14 12:42:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":351273,"visible":true,"origin":"","legend":"","description":"","filename":"NoiseFilteringKabirVaiSLNCS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7849525/v1_covered_4377089d-c89e-4315-84a9-f231dc18b25d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Robust Classifier for Label Noise Using Random Forest Kernel\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Dhaka","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":"Random forest, Noise filtering, Robust machine learning, Noisy data","lastPublishedDoi":"10.21203/rs.3.rs-7849525/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7849525/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMany real-world classification datasets suffer from the presence of label noise that negatively impacts classification performance. 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