{"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/multi-value-rule-sets-for-interpretable","title":"Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations","arxiv_id":null,"date":"2018-12-01","proceeding":"NeurIPS 2018 12","authors":["Tong Wang"],"abstract":"We present the Multi-value Rule Set (MRS) for interpretable\nclassification with feature efficient presentations. Compared to\nrule sets built from single-value rules, MRS adopts a more\ngeneralized form of association rules that allows multiple values\nin a condition. Rules of this form are more concise than classical\nsingle-value rules in capturing and describing patterns in data.\nOur formulation also pursues a higher efficiency of feature utilization,\nwhich reduces possible cost in data collection and storage.\nWe propose a Bayesian framework for formulating an MRS model\nand develop an efficient inference method for learning a maximum\na posteriori, incorporating theoretically grounded bounds to iteratively\nreduce the search space and improve the search efficiency.\nExperiments on synthetic and real-world data demonstrate that\nMRS models have significantly smaller complexity and fewer features\nthan baseline models while being competitive in predictive\naccuracy.","url_abs":"http://papers.nips.cc/paper/8281-multi-value-rule-sets-for-interpretable-classification-with-feature-efficient-representations","url_pdf":"http://papers.nips.cc/paper/8281-multi-value-rule-sets-for-interpretable-classification-with-feature-efficient-representations.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":"multi-value-rule-sets-for-interpretable","repo_url":"https://github.com/wangtongada/MRS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"form","task_name":"Form"},{"task_slug":"classification","task_name":"General 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}