{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/queue-based-resampling-for-online-class","title":"Queue-based Resampling for Online Class Imbalance Learning","arxiv_id":"1809.10388","date":"2018-09-27","proceeding":null,"authors":["Kleanthis Malialis","Christos G. Panayiotou","Marios M. Polycarpou"],"abstract":"Online class imbalance learning constitutes a new problem and an emerging\nresearch topic that focusses on the challenges of online learning under class\nimbalance and concept drift. Class imbalance deals with data streams that have\nvery skewed distributions while concept drift deals with changes in the class\nimbalance status. Little work exists that addresses these challenges and in\nthis paper we introduce queue-based resampling, a novel algorithm that\nsuccessfully addresses the co-existence of class imbalance and concept drift.\nThe central idea of the proposed resampling algorithm is to selectively include\nin the training set a subset of the examples that appeared in the past. Results\non two popular benchmark datasets demonstrate the effectiveness of queue-based\nresampling over state-of-the-art methods in terms of learning speed and\nquality.","url_abs":"http://arxiv.org/abs/1809.10388v2","url_pdf":"http://arxiv.org/pdf/1809.10388v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"queue-based-resampling-for-online-class","repo_url":"https://github.com/kmalialis/queue_based_resampling","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}