The processing for label noise based on attribute reduction and two-step method

The processing for label noise based on attribute reduction and two-step method | 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 The processing for label noise based on attribute reduction and two-step method Xingyu Wu, Ping Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5122434/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Jan, 2025 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted 11 You are reading this latest preprint version Abstract Classification is a mainstream task in machine learning. To achieve good classification results, there are many aspects to consider. Among them, label noise is the most direct and fundamental problem. Nowadays research targets the processing of label noise in numerous aspects, including correction, filtering and enhanced robustness methods. All these methods have improved the classification accuracy to some extent. However, the above studies consider only one approach to label noise, such as solely focusing on filtering or exclusively on correction. Label noise is complex and it is singular to consider only one method to deal with it. For example, contaminated data in a certain class and noise belonging to this class, both belong to the label noise problems, but with completely different distributions and treatments. This requires us to discuss the situations separately and to propose different processes. In this paper, we take this into account and propose a noise processing method that combines revision and filtration (RF). The RF method can follow the different distributions of label noise and perform targeted processes, which is more effective and comprehensive. It can maintain the original data distribution and remove noise as much as possible. On the other hand, high-dimensional datasets are encountered when dealing with label noise. The attribute values of the dataset will be abnormal due to the presence of label noise. Therefore, we suggest an attribute reduction method for the case when label noise exists. The advantage is that it not only removes redundant attributes, but also eliminates attributes interfered with by noise, which is suitable for high-dimensional data with label noise. Experiments prove that our proposed RF algorithm is effective among three classifiers with multiple comparison algorithms. Performing attribute reduction also improves classification accuracy significantly. Classification Label noise Noise revision Noise filtration Attribute reduction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Jan, 2025 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted Editorial decision: Revision requested 29 Oct, 2024 Reviews received at journal 28 Oct, 2024 Reviews received at journal 12 Oct, 2024 Reviews received at journal 12 Oct, 2024 Reviewers agreed at journal 04 Oct, 2024 Reviewers agreed at journal 03 Oct, 2024 Reviewers agreed at journal 03 Oct, 2024 Reviewers invited by journal 03 Oct, 2024 Editor assigned by journal 02 Oct, 2024 Submission checks completed at journal 23 Sep, 2024 First submitted to journal 20 Sep, 2024 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-5122434","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":371538866,"identity":"ac096509-be85-40b4-9090-713d1d4e2ccb","order_by":0,"name":"Xingyu Wu","email":"","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Xingyu","middleName":"","lastName":"Wu","suffix":""},{"id":371538867,"identity":"124a99e3-bdcc-4e9e-bb60-19c05f65309c","order_by":1,"name":"Ping Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYFACHgZmEMUvAeEyNhCrRUJyBpA8QJIWgxvEajHnP3tMurDtTp3x7R6zxx8YbGQ3HGB+9gCfFsuGc2nSM9ueSZjdOWNucIAhzXjDATZzA3xaDA72mEnzth2WMLuRYyZxgOFw4oYDPGwSeLUc5oFoMZ4B1vKfCC3HoFoMJMBaDhDWYtnDY2w949wzyRl3jpUbnDFINp55mM0MrxZz/jOGtwvK7vDzz27e9qCiwk6273jzM/wOg1AHQAQbhMuMTz2mllEwCkbBKBgFWAAAsXRI0ZlbgkIAAAAASUVORK5CYII=","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":true,"prefix":"","firstName":"Ping","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-09-20 09:26:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5122434/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5122434/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13042-025-02546-8","type":"published","date":"2025-01-22T15:58:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74858856,"identity":"b145ffa6-3477-4647-bc91-eb81251b50fa","added_by":"auto","created_at":"2025-01-27 16:13:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":931366,"visible":true,"origin":"","legend":"","description":"","filename":"submittedarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5122434/v1_covered_0d066810-fdad-426c-983a-1ad805dab939.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The processing for label noise based on attribute reduction and two-step method","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":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Classification, Label noise, Noise revision, Noise filtration, Attribute reduction","lastPublishedDoi":"10.21203/rs.3.rs-5122434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5122434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClassification is a mainstream task in machine learning. 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