{"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/aggressive-sampling-for-multi-class-to-binary","title":"Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification","arxiv_id":"1701.06511","date":"2017-01-23","proceeding":"NeurIPS 2017 12","authors":["Bikash Joshi","Massih-Reza Amini","Ioannis Partalas","Franck Iutzeler","Yury Maximov"],"abstract":"We address the problem of multi-class classification in the case where the\nnumber of classes is very large. We propose a double sampling strategy on top\nof a multi-class to binary reduction strategy, which transforms the original\nmulti-class problem into a binary classification problem over pairs of\nexamples. The aim of the sampling strategy is to overcome the curse of\nlong-tailed class distributions exhibited in majority of large-scale\nmulti-class classification problems and to reduce the number of pairs of\nexamples in the expanded data. We show that this strategy does not alter the\nconsistency of the empirical risk minimization principle defined over the\ndouble sample reduction. Experiments are carried out on DMOZ and Wikipedia\ncollections with 10,000 to 100,000 classes where we show the efficiency of the\nproposed approach in terms of training and prediction time, memory consumption,\nand predictive performance with respect to state-of-the-art approaches.","url_abs":"http://arxiv.org/abs/1701.06511v3","url_pdf":"http://arxiv.org/pdf/1701.06511v3.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":"aggressive-sampling-for-multi-class-to-binary","repo_url":"https://github.com/bikash617/Aggressive-Sampling-for-Multi-class-to-BinaryReduction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-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}