{"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/cgmos-certainty-guided-minority-oversampling","title":"CGMOS: Certainty Guided Minority OverSampling","arxiv_id":"1607.06525","date":"2016-07-21","proceeding":null,"authors":["Xi Zhang","Di Ma","Lin Gan","Shanshan Jiang","Gady Agam"],"abstract":"Handling imbalanced datasets is a challenging problem that if not treated\ncorrectly results in reduced classification performance. Imbalanced datasets\nare commonly handled using minority oversampling, whereas the SMOTE algorithm\nis a successful oversampling algorithm with numerous extensions. SMOTE\nextensions do not have a theoretical guarantee during training to work better\nthan SMOTE and in many instances their performance is data dependent. In this\npaper we propose a novel extension to the SMOTE algorithm with a theoretical\nguarantee for improved classification performance. The proposed approach\nconsiders the classification performance of both the majority and minority\nclasses. In the proposed approach CGMOS (Certainty Guided Minority\nOverSampling) new data points are added by considering certainty changes in the\ndataset. The paper provides a proof that the proposed algorithm is guaranteed\nto work better than SMOTE for training data. Further experimental results on 30\nreal-world datasets show that CGMOS works better than existing algorithms when\nusing 6 different classifiers.","url_abs":"http://arxiv.org/abs/1607.06525v1","url_pdf":"http://arxiv.org/pdf/1607.06525v1.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":"cgmos-certainty-guided-minority-oversampling","repo_url":"https://github.com/xzhang311/CGMOS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"smote","method_name":"SMOTE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}