Adaptive Ensemble Learning for Drift Detection and Mitigation in Non-Stationary Data Streams

Adaptive Ensemble Learning for Drift Detection and Mitigation in Non-Stationary Data Streams | 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 Ensemble Learning for Drift Detection and Mitigation in Non-Stationary Data Streams Dr. Palaparthi Kalyan Kumar, Dr. Sajja Suneel, Dr. Sateesh Kumar Reddy Chirasani, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9052743/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 Non-stationary data streams present a challenge to learning because concept drift decreases the performance of the fixed machine learning models. In this paper, we will suggest a severity-conscious adaptive ensemble framework to support robust online learning with changing data distribution. The approach combines implicit drift detection, adaptive weights of learners and dynamic replacement of learners as a single ensemble model. It is proposed to determine the extent of performance degradation with a new Drift Severity Index (DSI) that can be used to determine the strength of mitigation, and Drift Recovery Time (DRT) is introduced as a new index to measure the speed of adaptation. Moreover, a learner archive system allows managing the recurrent drift effectively by reactivating models. The experimental findings of the synthetic data streams with sudden, gradual and periodic drift prove that the proposed framework maintains stable performance and recovers fast when distributional change is severe. The fact that it has a low per-instance computational complexity renders the approach appropriate in real-time data stream applications. Concept Drift Data Stream Mining Adaptive Ensemble Learning Severity-Aware Mitigation Drift Severity Index (DSI) Drift Recovery Time (DRT) 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-9052743","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619560938,"identity":"9bee04ae-fe65-4eff-b260-d8bad240b919","order_by":0,"name":"Dr. Palaparthi Kalyan 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