{"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/meta-learning-for-resampling-recommendation","title":"Meta-Learning for Resampling Recommendation Systems","arxiv_id":"1706.02289","date":"2017-06-06","proceeding":null,"authors":["Smolyakov Dmitry","Alexander Korotin","Pavel Erofeev","Artem Papanov","Evgeny Burnaev"],"abstract":"One possible approach to tackle the class imbalance in classification tasks\nis to resample a training dataset, i.e., to drop some of its elements or to\nsynthesize new ones. There exist several widely-used resampling methods. Recent\nresearch showed that the choice of resampling method significantly affects the\nquality of classification, which raises resampling selection problem.\nExhaustive search for optimal resampling is time-consuming and hence it is of\nlimited use. In this paper, we describe an alternative approach to the\nresampling selection. We follow the meta-learning concept to build resampling\nrecommendation systems, i.e., algorithms recommending resampling for datasets\non the basis of their properties.","url_abs":"http://arxiv.org/abs/1706.02289v4","url_pdf":"http://arxiv.org/pdf/1706.02289v4.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":"meta-learning-for-resampling-recommendation","repo_url":"https://github.com/papart/res-recsyst","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"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}