{"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/learning-interpretable-rules-for-multi-label","title":"Learning Interpretable Rules for Multi-label Classification","arxiv_id":"1812.00050","date":"2018-11-30","proceeding":null,"authors":["Eneldo Loza Mencía","Johannes Fürnkranz","Eyke Hüllermeier","Michael Rapp"],"abstract":"Multi-label classification (MLC) is a supervised learning problem in which,\ncontrary to standard multiclass classification, an instance can be associated\nwith several class labels simultaneously. In this chapter, we advocate a\nrule-based approach to multi-label classification. Rule learning algorithms are\noften employed when one is not only interested in accurate predictions, but\nalso requires an interpretable theory that can be understood, analyzed, and\nqualitatively evaluated by domain experts. Ideally, by revealing patterns and\nregularities contained in the data, a rule-based theory yields new insights in\nthe application domain. Recently, several authors have started to investigate\nhow rule-based models can be used for modeling multi-label data. Discussing\nthis task in detail, we highlight some of the problems that make rule learning\nconsiderably more challenging for MLC than for conventional classification.\nWhile mainly focusing on our own previous work, we also provide a short\noverview of related work in this area.","url_abs":"http://arxiv.org/abs/1812.00050v2","url_pdf":"http://arxiv.org/pdf/1812.00050v2.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":"learning-interpretable-rules-for-multi-label","repo_url":"https://github.com/keelm/SeCo-MLC","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":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"multi-label-classification","task_name":"Multi-Label 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}