eprintid: 9249 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/00/92/49 datestamp: 2023-11-09 16:21:13 lastmod: 2023-11-09 16:21:13 status_changed: 2023-11-09 16:14:38 type: article metadata_visibility: show creators_name: Sakai, H. creators_name: Nakata, M. creators_name: Watada, J. title: A proposal of machine learning by rule generation from tables with non-deterministic information and its prototype system ispublished: pub keywords: Artificial intelligence; Learning systems; Maximum likelihood; Maximum likelihood estimation; Rough set theory, Apriori algorithms; Attribute values; Logical frameworks; Non-deterministic information; Prototype; Prototype system; Rule generation; Uncertainty, Education note: cited By 2; Conference of International Joint Conference on Rough Sets, IJCRS 2017 ; Conference Date: 3 July 2017 Through 7 July 2017; Conference Code:193629 abstract: A logical framework on Machine Learning by Rule Generation (MLRG) from tables with non-deterministic information is proposed, and its prototype system in SQL is implemented. In MLRG, the certain rules defined in Rough Non-deterministic Information Analysis (RNIA) are obtained at first, and each uncertain attribute value is estimated so as to cause the certain rules as many as possible, because the certain rules show us the most reliable information. This strategy is similar to the maximum likelihood estimation in statistics. By repeating this process, a standard table and the rules in its table are learned (or estimated) from a given table with non-deterministic information. Even though it will be hard to know the actual unknown values, MLRG will give a plausible estimation value. © Springer International Publishing AG 2017. date: 2017 publisher: Springer Verlag official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022327980&doi=10.1007%2f978-3-319-60837-2_43&partnerID=40&md5=729710184c4b5656bfe528821d0ca9c2 id_number: 10.1007/978-3-319-60837-2₄₃ full_text_status: none publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) volume: 10313 pagerange: 535-551 refereed: TRUE isbn: 9783319608365 issn: 03029743 citation: Sakai, H. and Nakata, M. and Watada, J. (2017) A proposal of machine learning by rule generation from tables with non-deterministic information and its prototype system. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10313 . pp. 535-551. ISSN 03029743