Quadratic Surface Twin Support Vector Machine for Imbalanced Data

Quadratic Surface Twin Support Vector Machine for Imbalanced 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 Quadratic Surface Twin Support Vector Machine for Imbalanced Data Hossein Moosaei, Milan Hladik, Ahmad Mousavi, Zheming Gao, Haojie Fu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6591941/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Apr, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted 12 You are reading this latest preprint version Abstract Binary classification tasks with imbalanced classes pose significant challenges in machine learning. Traditional classifiers often struggle to accurately capture the characteristics of the minority class, resulting in biased models with subpar predictive performance. In this paper, we introduce a novel approach to tackle this issue by leveraging Universum points to support the minority class within quadratic twin support vector machine models. Unlike traditional classifiers, our models utilize quadratic surfaces instead of hyperplanes for binary classification, providing greater flexibility in modeling complex decision boundaries. By incorporating Universum points, our approach enhances classification accuracy and generalization performance on imbalanced datasets. We generated four artificial datasets to demonstrate the flexibility of the proposed methods. Additionally, we validated the effectiveness of our approach through empirical evaluations on benchmark datasets, showing superior performance compared to conventional classifiers and existing methods for imbalanced classification. Binary classification Quadratic support vector machine Twin support vector machine Class imbalance Universum data Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Apr, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted Editorial decision: Revision requested 11 Aug, 2025 Reviews received at journal 15 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviewers agreed at journal 06 Jul, 2025 Reviews received at journal 05 Jul, 2025 Reviewers agreed at journal 03 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers agreed at journal 15 Jun, 2025 Reviewers invited by journal 12 Jun, 2025 Editor assigned by journal 11 May, 2025 Submission checks completed at journal 06 May, 2025 First submitted to journal 05 May, 2025 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. 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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-6591941","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470138249,"identity":"78a28515-259b-4489-a921-2d636f8e3407","order_by":0,"name":"Hossein Moosaei","email":"","orcid":"","institution":"Jan Evangelista Purkyně University in Ústí nad Labem","correspondingAuthor":false,"prefix":"","firstName":"Hossein","middleName":"","lastName":"Moosaei","suffix":""},{"id":470138250,"identity":"f56e3e71-c007-47b2-b525-47207bd036c5","order_by":1,"name":"Milan Hladik","email":"","orcid":"","institution":"Charles University","correspondingAuthor":false,"prefix":"","firstName":"Milan","middleName":"","lastName":"Hladik","suffix":""},{"id":470138251,"identity":"27b9d46a-4201-403d-88fb-597b3ac64778","order_by":2,"name":"Ahmad Mousavi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYPACCTkwlQDhGRClxZhkLQyJDUgc/Fp023uMP/xss0jvn3b42IcHNXZ5DOzN2yTwaTE7c8ZMsrdNInfG7bTkGQnHkosZeI6V4ddyI8eMgeeMRG7D7RxjoPOYExskcswIaTH++OeMRLr87fzPQC31iQ3ybwhqMZDmqZBIMLidwwzUchhoCw8BLWeOlUnLVEgYbrydZsyQcOx4YhtPWrEFXi3Hmzd/fGNQJy93O/kx44+a6sR+9sMbb+DTggnYSFM+CkbBKBgFowAbAADw20djCMYiyQAAAABJRU5ErkJggg==","orcid":"","institution":"American University","correspondingAuthor":true,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Mousavi","suffix":""},{"id":470138252,"identity":"d05011a2-f225-4b01-be28-b767e1e111f8","order_by":3,"name":"Zheming Gao","email":"","orcid":"","institution":"Rice University","correspondingAuthor":false,"prefix":"","firstName":"Zheming","middleName":"","lastName":"Gao","suffix":""},{"id":470138253,"identity":"c71b58ce-4732-4c64-a8ec-373a59def30a","order_by":4,"name":"Haojie Fu","email":"","orcid":"","institution":"Northeastern University","correspondingAuthor":false,"prefix":"","firstName":"Haojie","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2025-05-05 07:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6591941/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6591941/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13042-026-03059-8","type":"published","date":"2026-04-13T15:59:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":107350883,"identity":"bd636643-f834-48fb-95f2-fcb5b48401f0","added_by":"auto","created_at":"2026-04-20 16:06:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2010325,"visible":true,"origin":"","legend":"","description":"","filename":"QuadraticSurfaceTwinSVMImbalanceddata.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6591941/v1_covered_29309504-ff51-48bd-b9e9-f3d74d0ffe75.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eQuadratic Surface Twin Support Vector Machine for Imbalanced Data\u003c/p\u003e","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":"Binary classification, Quadratic support vector machine, Twin support vector machine, Class imbalance, Universum data","lastPublishedDoi":"10.21203/rs.3.rs-6591941/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6591941/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Binary classification tasks with imbalanced classes pose significant challenges in machine learning. 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