{"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/box-drawings-for-learning-with-imbalanced","title":"Box Drawings for Learning with Imbalanced Data","arxiv_id":"1403.3378","date":"2014-03-13","proceeding":null,"authors":["Siong Thye Goh","Cynthia Rudin"],"abstract":"The vast majority of real world classification problems are imbalanced,\nmeaning there are far fewer data from the class of interest (the positive\nclass) than from other classes. We propose two machine learning algorithms to\nhandle highly imbalanced classification problems. The classifiers constructed\nby both methods are created as unions of parallel axis rectangles around the\npositive examples, and thus have the benefit of being interpretable. The first\nalgorithm uses mixed integer programming to optimize a weighted balance between\npositive and negative class accuracies. Regularization is introduced to improve\ngeneralization performance. The second method uses an approximation in order to\nassist with scalability. Specifically, it follows a \\textit{characterize then\ndiscriminate} approach, where the positive class is characterized first by\nboxes, and then each box boundary becomes a separate discriminative classifier.\nThis method has the computational advantages that it can be easily\nparallelized, and considers only the relevant regions of feature space.","url_abs":"http://arxiv.org/abs/1403.3378v2","url_pdf":"http://arxiv.org/pdf/1403.3378v2.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":"box-drawings-for-learning-with-imbalanced","repo_url":"https://github.com/wpriyadarshani/Fastbox","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"imbalanced-classification","task_name":"imbalanced classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}