Adaptive Synthetic Minority Oversampling Technique with Density-Guided Noise Injection and Local Density Adaptation

Adaptive Synthetic Minority Oversampling Technique with Density-Guided Noise Injection and Local Density Adaptation | 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 Adaptive Synthetic Minority Oversampling Technique with Density-Guided Noise Injection and Local Density Adaptation Zaitinkhuma Thihlum, Vanlal hruaia, V. D. Ambeth Kumar, R Chawngsangpuii This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7945642/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 remains a persistent challenge in supervised learning, often leading to biased classifiers and poor detection of minority instances. This paper introduces Adaptive Synthetic Minority Oversampling Technique with Guided Density (AdaptiveSMOTEGD), a novel method that integrates local density-based sparsity detection, tunable Gaussian noise injection, and domain-specific constraint preservation. Unlike conventional methods such as Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling Approach (ADASYN), Borderline-SMOTE, Synthetic Minority Over-sampling Technique for Nominal and Continuous features (SMOTENC), Support Vector Machine SMOTE (SVMSMOTE), and KMeans-SMOTE, the proposed approach selectively targets sparse minority regions while avoiding degradation in dense areas. It also supports datasets with purely numerical features as well as those containing both numerical and categorical attributes. Experimental evaluation on eight numerical-only and six mixed-type benchmark datasets using Light Gradient Boosting Machine (LightGBM) demonstrates that AdaptiveSMOTEGD consistently achieves competitive or superior performance in F1-score, recall, Matthews Correlation Coefficient (MCC), and area under the precision-recall curve (AUC-PR), particularly under highly imbalanced and noisy conditions. Statistical analysis confirms significant improvements in recall for both numerical-only and mixed datasets, establishing AdaptiveSMOTEGD as a robust, scalable, and versatile solution for real-world imbalanced classification problems. imbalanced classification synthetic oversampling mixed feature handling noise aware sampling 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. 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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-7945642","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534858691,"identity":"0e58f37d-5877-49a9-9c31-d5d191d6d8f7","order_by":0,"name":"Zaitinkhuma Thihlum","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYFACxgYQKcfYDqINLIjXYszccwCkRYJ4uxLbZySAaCK0yLcfbvx0o+ZwYu/M51c3/CiQYOBv707A76yexGbpnGOHjWfOzim72QN0mMSZsxvwamFmSGyQzmE7LLtxdk7aDR6gFgOJXPxa2PgfNv/O+XeYcf/NM2k3/xCjhUcisU06t+2wYuMM9mO3ibJFQuJhm3VuX7oxY08O220ZAwkegn6R709/fDvnmzUwKo8/u/nmj40cf3svfi1Q0AxyowHYpcQoB4E6IGZ/QKzqUTAKRsEoGGEAAEJ1S2ohcQenAAAAAElFTkSuQmCC","orcid":"","institution":"Mizoram University","correspondingAuthor":true,"prefix":"","firstName":"Zaitinkhuma","middleName":"","lastName":"Thihlum","suffix":""},{"id":534858692,"identity":"d4fe5f7f-e3e5-4ca5-81fa-e6f096d81453","order_by":1,"name":"Vanlal hruaia","email":"","orcid":"","institution":"Mizoram University","correspondingAuthor":false,"prefix":"","firstName":"Vanlal","middleName":"","lastName":"hruaia","suffix":""},{"id":534858693,"identity":"16f7863c-35dc-406b-a740-b31096c9709f","order_by":2,"name":"V. 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Adaptation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"imbalanced classification, synthetic oversampling, mixed feature handling, noise aware 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This paper introduces Adaptive Synthetic Minority Oversampling Technique with Guided Density (AdaptiveSMOTEGD), a novel method that integrates local density-based sparsity detection, tunable Gaussian noise injection, and domain-specific constraint preservation. Unlike conventional methods such as Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling Approach (ADASYN), Borderline-SMOTE, Synthetic Minority Over-sampling Technique for Nominal and Continuous features (SMOTENC), Support Vector Machine SMOTE (SVMSMOTE), and KMeans-SMOTE, the proposed approach selectively targets sparse minority regions while avoiding degradation in dense areas. It also supports datasets with purely numerical features as well as those containing both numerical and categorical attributes. Experimental evaluation on eight numerical-only and six mixed-type benchmark datasets using Light Gradient Boosting Machine (LightGBM) demonstrates that AdaptiveSMOTEGD consistently achieves competitive or superior performance in F1-score, recall, Matthews Correlation Coefficient (MCC), and area under the precision-recall curve (AUC-PR), particularly under highly imbalanced and noisy conditions. 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