{"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/on-robust-trimming-of-bayesian-network","title":"On Robust Trimming of Bayesian Network Classifiers","arxiv_id":"1805.11243","date":"2018-05-29","proceeding":null,"authors":["YooJung Choi","Guy Van Den Broeck"],"abstract":"This paper considers the problem of removing costly features from a Bayesian\nnetwork classifier. We want the classifier to be robust to these changes, and\nmaintain its classification behavior. To this end, we propose a closeness\nmetric between Bayesian classifiers, called the expected classification\nagreement (ECA). Our corresponding trimming algorithm finds an optimal subset\nof features and a new classification threshold that maximize the expected\nagreement, subject to a budgetary constraint. It utilizes new theoretical\ninsights to perform branch-and-bound search in the space of feature sets, while\ncomputing bounds on the ECA. Our experiments investigate both the runtime cost\nof trimming and its effect on the robustness and accuracy of the final\nclassifier.","url_abs":"http://arxiv.org/abs/1805.11243v1","url_pdf":"http://arxiv.org/pdf/1805.11243v1.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":"on-robust-trimming-of-bayesian-network","repo_url":"https://github.com/UCLA-StarAI/TrimBN","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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}