{"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/extremely-randomized-cnets-for-multi-label","title":"Extremely Randomized CNets for Multi-label Classification","arxiv_id":null,"date":"2018-10-01","proceeding":"XVIIth International Conference of the Italian Association for Artificial Intelligence 2018 10","authors":["Teresa M.A. Basile","Nicola Di Mauro","Floriana Esposito"],"abstract":"Multi-label classification (MLC) is a challenging task in ma-chine learning consisting in the prediction of multiple labels associated with  a  single  instance.  Promising  approaches  for  MLC  are  those  able to capture label dependencies by learning a single probabilistic model—differently  from  other  competitive  approaches  requiring  to  learn  many models. The model is then exploited to compute the most probable label configuration given the observed attributes. Cutset Networks (CNets) are density estimators leveraging context-specific independencies providing exact inference in polynomial time. The recently introduced Extremely Randomized CNets (XCNets) reduce the structure learning complexity making able to learn ensembles of XCNets outperforming state-of-the-art  density  estimators.  In  this  paper  we  employ  XCNets  for  MLC  by exploiting efficient Most Probable Explanations (MPE). An experimental evaluation on real-world datasets shows how the proposed approach is competitive w.r.t. other sophisticated methods for MLC","url_abs":"https://link.springer.com/chapter/10.1007/978-3-030-03840-3_25","url_pdf":"http://www.di.uniba.it/~ndm/pubs/basile18aixia.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":"extremely-randomized-cnets-for-multi-label","repo_url":"https://github.com/nicoladimauro/mlxcnet","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"extremely-randomized-cnets-for-multi-label","repo_url":"https://github.com/2023-MindSpore-1/ms-code-210/tree/main/Cnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"extremely-randomized-cnets-for-multi-label","repo_url":"https://github.com/2023-MindSpore-4/Code8/tree/main/Cnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"extremely-randomized-cnets-for-multi-label","repo_url":"https://github.com/MindSpore-paper-code-3/code6/tree/main/Cnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"density-estimation","task_name":"Density Estimation"},{"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}