{"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/f-measure-maximization-in-multi-label","title":"F-measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets","arxiv_id":"1604.07759","date":"2016-04-26","proceeding":null,"authors":["Maxime Gasse","Alex Aussem"],"abstract":"We discuss a method to improve the exact F-measure maximization algorithm\ncalled GFM, proposed in (Dembczynski et al. 2011) for multi-label\nclassification, assuming the label set can be can partitioned into\nconditionally independent subsets given the input features. If the labels were\nall independent, the estimation of only $m$ parameters ($m$ denoting the number\nof labels) would suffice to derive Bayes-optimal predictions in $O(m^2)$\noperations. In the general case, $m^2+1$ parameters are required by GFM, to\nsolve the problem in $O(m^3)$ operations. In this work, we show that the number\nof parameters can be reduced further to $m^2/n$, in the best case, assuming the\nlabel set can be partitioned into $n$ conditionally independent subsets. As\nthis label partition needs to be estimated from the data beforehand, we use\nfirst the procedure proposed in (Gasse et al. 2015) that finds such partition\nand then infer the required parameters locally in each label subset. The latter\nare aggregated and serve as input to GFM to form the Bayes-optimal prediction.\nWe show on a synthetic experiment that the reduction in the number of\nparameters brings about significant benefits in terms of performance.","url_abs":"http://arxiv.org/abs/1604.07759v3","url_pdf":"http://arxiv.org/pdf/1604.07759v3.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":"f-measure-maximization-in-multi-label","repo_url":"https://github.com/gasse/fgfm-toy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"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}