Extended Hybrid Resampling Architecture for Addressing Imbalanced Datasets in Multi-Label Classification

Extended Hybrid Resampling Architecture for Addressing Imbalanced Datasets in Multi-Label Classification | 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 Extended Hybrid Resampling Architecture for Addressing Imbalanced Datasets in Multi-Label Classification Mediana Aryuni, Chastine Fatichah, Anny Yuniarti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9080792/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 Class imbalance is a common problem in multi-label classification (MLC). This problem can reduce the predictive accuracy of classifiers. To address this issue, recent studies have proposed hybrid resampling approaches that combine data-level balancing techniques in MLC. The goal of this research is to improve the performance of multi-label classifiers on imbalanced datasets by developing and testing extended hybrid resampling architecture based on REMEDIAL-Hybrid-with-Resampling (R-HwR), R-HwR-ROS and R-HwR-SMT. Hybrid resampling architecture was proposed by extending R-HwR-ROS and R-HwR-SMT with resampling strategies such as Multi-Label edited Nearest Neighbor (MLeNN), Multi-Label Tomek Link (MLTL) and Multi-Label Random Under Sampling (MLRUS) using five multi-label classifiers: Binary Relevance (BR), Classifier Chain (CC), Calibrated Label Ranking (CLR), Label Powerset (LP), and Multi-Label k-Nearest Neighbor (ML-kNN). The classifier performances were evaluated using Micro/Macro-F1, Hamming Loss, and statistical tests such as the Wilcoxon signed-rank and Friedman tests to identify significant improvements and optimal setups across several benchmark datasets. The hybrid of Base + MLTL significantly improved R-HwR-ROS and R-HwR-SMT, whereas Base + MLeNN significantly enhanced R-HwR-ROS (p < 0.05). Specifically, CC has emerged as the most reliable classifier. In R-HwR-ROS, MLeNN outperformed other combinations with the BR, CC, and CLR classifiers, whereas MLTL outperformed the other combinations with the LP and ML-kNN classifiers. In R-HwR-SMT, MLTL outperformed the other combinations for all classifiers. Hybrid resampling algorithms, including MLeNN and MLTL, greatly boost classifier robustness and balance across varied datasets. Multi-label classification class imbalance hybrid resampling R-HwR statistical analysis 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-9080792","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612186592,"identity":"9de12fb9-4b4f-46a7-ac98-9fc5424b5ec3","order_by":0,"name":"Mediana Aryuni","email":"","orcid":"","institution":"Institut Teknologi Sepuluh Nopember","correspondingAuthor":false,"prefix":"","firstName":"Mediana","middleName":"","lastName":"Aryuni","suffix":""},{"id":612186593,"identity":"e0a8cc1e-6419-4528-92af-ce673260cebc","order_by":1,"name":"Chastine Fatichah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie2RoYrDQBCGJ6wtrN2a5BWmBKLuYabmaiqqjlNpIKKyNnB9i0L1HAONyQNUVFw5OHUiUFNRcZNwFCpuU3liP7Fi4eP//12AQOA/4roDAayJPoB+L3k0pLAq49IgED2sdEGsyi3Gp9i38vPcLo5xWhuA0yUHu+KIv30hx33mGL/STFQhEnANwfvGo6CjTLfIdCeWVdGOBwDxFUM3O7eqLLdln5JDMqzMUYsJoekVAzikuMP8xTUok0q3MD3LaNJMC+8WW8227etVEruW6HR5yuO4Fml9L3YHQ/8jUfGoEAgEAoE/+AGA6E3O5VOQ8AAAAABJRU5ErkJggg==","orcid":"","institution":"Institut Teknologi Sepuluh Nopember","correspondingAuthor":true,"prefix":"","firstName":"Chastine","middleName":"","lastName":"Fatichah","suffix":""},{"id":612186594,"identity":"a163a8b3-df92-4fce-86f2-1ef2dcb91edf","order_by":2,"name":"Anny Yuniarti","email":"","orcid":"","institution":"Institut Teknologi Sepuluh Nopember","correspondingAuthor":false,"prefix":"","firstName":"Anny","middleName":"","lastName":"Yuniarti","suffix":""}],"badges":[],"createdAt":"2026-03-10 07:53:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9080792/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9080792/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108007301,"identity":"14316dfe-ee28-4569-a3a6-e5c16e31d55d","added_by":"auto","created_at":"2026-04-28 12:59:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1021997,"visible":true,"origin":"","legend":"","description":"","filename":"PaperMediana100326.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9080792/v1_covered_7375c364-85e2-4319-bf65-abc059288b50.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Extended Hybrid Resampling Architecture for Addressing Imbalanced Datasets in Multi-Label Classification","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":"Multi-label classification, class imbalance, hybrid resampling, R-HwR, statistical analysis","lastPublishedDoi":"10.21203/rs.3.rs-9080792/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9080792/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClass imbalance is a common problem in multi-label classification (MLC). This problem can reduce the predictive accuracy of classifiers. To address this issue, recent studies have proposed hybrid resampling approaches that combine data-level balancing techniques in MLC. The goal of this research is to improve the performance of multi-label classifiers on imbalanced datasets by developing and testing extended hybrid resampling architecture based on REMEDIAL-Hybrid-with-Resampling (R-HwR), R-HwR-ROS and R-HwR-SMT. Hybrid resampling architecture was proposed by extending R-HwR-ROS and R-HwR-SMT with resampling strategies such as Multi-Label edited Nearest Neighbor (MLeNN), Multi-Label Tomek Link (MLTL) and Multi-Label Random Under Sampling (MLRUS) using five multi-label classifiers: Binary Relevance (BR), Classifier Chain (CC), Calibrated Label Ranking (CLR), Label Powerset (LP), and Multi-Label k-Nearest Neighbor (ML-kNN). The classifier performances were evaluated using Micro/Macro-F1, Hamming Loss, and statistical tests such as the Wilcoxon signed-rank and Friedman tests to identify significant improvements and optimal setups across several benchmark datasets. The hybrid of Base\u0026thinsp;+\u0026thinsp;MLTL significantly improved R-HwR-ROS and R-HwR-SMT, whereas Base\u0026thinsp;+\u0026thinsp;MLeNN significantly enhanced R-HwR-ROS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, CC has emerged as the most reliable classifier. In R-HwR-ROS, MLeNN outperformed other combinations with the BR, CC, and CLR classifiers, whereas MLTL outperformed the other combinations with the LP and ML-kNN classifiers. In R-HwR-SMT, MLTL outperformed the other combinations for all classifiers. Hybrid resampling algorithms, including MLeNN and MLTL, greatly boost classifier robustness and balance across varied datasets.\u003c/p\u003e","manuscriptTitle":"Extended Hybrid Resampling Architecture for Addressing Imbalanced Datasets in Multi-Label Classification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 10:59:31","doi":"10.21203/rs.3.rs-9080792/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"01dc5da6-b1e5-4ce7-862d-6d66b5371b16","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T04:55:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 10:59:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9080792","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9080792","identity":"rs-9080792","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}