{"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/inducing-generalized-multi-label-rules-with","title":"Inducing Generalized Multi-Label Rules with Learning Classifier Systems","arxiv_id":"1512.07982","date":"2015-12-25","proceeding":null,"authors":["Fani A. Tzima","Miltiadis Allamanis","Alexandros Filotheou","Pericles A. Mitkas"],"abstract":"In recent years, multi-label classification has attracted a significant body\nof research, motivated by real-life applications, such as text classification\nand medical diagnoses. Although sparsely studied in this context, Learning\nClassifier Systems are naturally well-suited to multi-label classification\nproblems, whose search space typically involves multiple highly specific\nniches. This is the motivation behind our current work that introduces a\ngeneralized multi-label rule format -- allowing for flexible label-dependency\nmodeling, with no need for explicit knowledge of which correlations to search\nfor -- and uses it as a guide for further adapting the general Michigan-style\nsupervised Learning Classifier System framework. The integration of the\naforementioned rule format and framework adaptations results in a novel\nalgorithm for multi-label classification whose behavior is studied through a\nset of properly defined artificial problems. The proposed algorithm is also\nthoroughly evaluated on a set of multi-label datasets and found competitive to\nother state-of-the-art multi-label classification methods.","url_abs":"http://arxiv.org/abs/1512.07982v1","url_pdf":"http://arxiv.org/pdf/1512.07982v1.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":"inducing-generalized-multi-label-rules-with","repo_url":"https://github.com/fanioula/mlslcs","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"},{"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}