{"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/an-optimization-approach-to-learning-falling","title":"An Optimization Approach to Learning Falling Rule Lists","arxiv_id":"1710.02572","date":"2017-10-06","proceeding":null,"authors":["Chaofan Chen","Cynthia Rudin"],"abstract":"A falling rule list is a probabilistic decision list for binary\nclassification, consisting of a series of if-then rules with antecedents in the\nif clauses and probabilities of the desired outcome (\"1\") in the then clauses.\nJust as in a regular decision list, the order of rules in a falling rule list\nis important -- each example is classified by the first rule whose antecedent\nit satisfies. Unlike a regular decision list, a falling rule list requires the\nprobabilities of the desired outcome (\"1\") to be monotonically decreasing down\nthe list. We propose an optimization approach to learning falling rule lists\nand \"softly\" falling rule lists, along with Monte-Carlo search algorithms that\nuse bounds on the optimal solution to prune the search space.","url_abs":"http://arxiv.org/abs/1710.02572v3","url_pdf":"http://arxiv.org/pdf/1710.02572v3.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":"an-optimization-approach-to-learning-falling","repo_url":"https://github.com/cfchen-duke/FRLOptimization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.02572","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}