DPDO:Dynamic Possion Disk Oversampling based on minority clusters within circular region for class imbalance problem

DPDO:Dynamic Possion Disk Oversampling based on minority clusters within circular region for class imbalance problem | 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 DPDO:Dynamic Possion Disk Oversampling based on minority clusters within circular region for class imbalance problem Runze Chen, Qiangkui Leng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8258317/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract In classification tasks, the number of samples in different classes may differ significantly, a phenomenon known as the class imbalance problem. A common approach to address this issue is the Synthetic Minority Oversampling Technique (SMOTE). It works by changing the data distribution instead of the classifier. However, many SMOTE-based improved methods still rely solely on local linear interpolation between a sample and its nearest neighbors, neglecting the impact of the global neighborhood structure on the sample synthesis process, thereby limiting the diversity and authenticity of the generated samples.To overcome this limitation, inspired by the concept of Poisson Disk Sampling, we proposes a Dynamic Poisson Disk Oversampling algorithm (DPDO) combining global neighborhood awareness with density-adaptive sampling control.First,DPDO removes noise samples by calculating the total distance of each sample to its K nearest neighbors and identifies clusters of points with similar features. Then, within these clustered points, it dynamically expands the oversampling region based on their structural constraints,generating new samples that adequately reflect the features of the minority class. Extensive experiments on 27 benchmark datasets, comparing DPDO with ten representative oversampling baselines, show that DPDO achieves superior performance in terms of F1-score and G-mean. The results indicate that DPDO can effectively alleviate class imbalance and produces more realistic, uniform, and diverse minority samples, by incorporating global neighborhood information. Class imbalance Linear interpolation Oversampling Poisson Disk Sampling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 05 Feb, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviewers invited by journal 14 Jan, 2026 Editor assigned by journal 06 Dec, 2025 Submission checks completed at journal 05 Dec, 2025 First submitted to journal 02 Dec, 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. 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-8258317","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576246428,"identity":"ca45e675-67f9-4fa1-8238-afb82055caf1","order_by":0,"name":"Runze Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Runze","middleName":"","lastName":"Chen","suffix":""},{"id":576246434,"identity":"8830c793-0325-4616-8cb8-143d0ca0476f","order_by":1,"name":"Qiangkui 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A common approach to address this issue is the Synthetic Minority Oversampling Technique (SMOTE). It works by changing the data distribution instead of the classifier. However, many SMOTE-based improved methods still rely solely on local linear interpolation between a sample and its nearest neighbors, neglecting the impact of the global neighborhood structure on the sample synthesis process, thereby limiting the diversity and authenticity of the generated samples.To overcome this limitation, inspired by the concept of Poisson Disk Sampling, we proposes a Dynamic Poisson Disk Oversampling algorithm (DPDO) combining global neighborhood awareness with density-adaptive sampling control.First,DPDO removes noise samples by calculating the total distance of each sample to its K nearest neighbors and identifies clusters of points with similar features. Then, within these clustered points, it dynamically expands the oversampling region based on their structural constraints,generating new samples that adequately reflect the features of the minority class. Extensive experiments on 27 benchmark datasets, comparing DPDO with ten representative oversampling baselines, show that DPDO achieves superior performance in terms of F1-score and G-mean. The results indicate that DPDO can effectively alleviate class imbalance and produces more realistic, uniform, and diverse minority samples, by incorporating global neighborhood information.\u003c/p\u003e","manuscriptTitle":"DPDO:Dynamic Possion Disk Oversampling based on minority clusters within circular region for class imbalance problem","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 08:26:01","doi":"10.21203/rs.3.rs-8258317/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-05T10:28:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217404115118167119457704173871507361349","date":"2026-01-18T08:59:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-15T02:49:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-06T08:40:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-05T06:28:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Knowledge and Information Systems","date":"2025-12-02T08:53:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"knowledge-and-information-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"kais","sideBox":"Learn more about [Knowledge and Information Systems](http://link.springer.com/journal/10115)","snPcode":"10115","submissionUrl":"https://submission.nature.com/new-submission/10115/3","title":"Knowledge and Information Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1cfe304b-20a7-4bf6-ba99-d6d4ecf33e7d","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T08:26:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 08:26:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8258317","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8258317","identity":"rs-8258317","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}