{"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/grafting-for-combinatorial-boolean-model","title":"Grafting for Combinatorial Boolean Model using Frequent Itemset Mining","arxiv_id":"1711.02478","date":"2017-11-07","proceeding":null,"authors":["Taito Lee","Shin Matsushima","Kenji Yamanishi"],"abstract":"This paper introduces the combinatorial Boolean model (CBM), which is defined\nas the class of linear combinations of conjunctions of Boolean attributes. This\npaper addresses the issue of learning CBM from labeled data. CBM is of high\nknowledge interpretability but na\\\"{i}ve learning of it requires exponentially\nlarge computation time with respect to data dimension and sample size. To\novercome this computational difficulty, we propose an algorithm GRAB (GRAfting\nfor Boolean datasets), which efficiently learns CBM within the\n$L_1$-regularized loss minimization framework. The key idea of GRAB is to\nreduce the loss minimization problem to the weighted frequent itemset mining,\nin which frequent patterns are efficiently computable. We employ benchmark\ndatasets to empirically demonstrate that GRAB is effective in terms of\ncomputational efficiency, prediction accuracy and knowledge discovery.","url_abs":"http://arxiv.org/abs/1711.02478v2","url_pdf":"http://arxiv.org/pdf/1711.02478v2.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":"grafting-for-combinatorial-boolean-model","repo_url":"https://gitlab.com/taitor/GRAB-experiments","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}