TY  - JOUR
KW  - Artificial intelligence; Learning systems; Maximum likelihood; Maximum likelihood estimation; Rough set theory
KW  -  Apriori algorithms; Attribute values; Logical frameworks; Non-deterministic information; Prototype; Prototype system; Rule generation; Uncertainty
KW  -  Education
ID  - scholars9249
N2  - 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.
VL  - 10313 
UR  - 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
A1  - Sakai, H.
A1  - Nakata, M.
A1  - Watada, J.
JF  - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Y1  - 2017///
TI  - A proposal of machine learning by rule generation from tables with non-deterministic information and its prototype system
SP  - 535
N1  - 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
AV  - none
EP  - 551
SN  - 03029743
PB  - Springer Verlag
ER  -