Proposal of a statistical test rule induction method by use of the decision table

• Published in: Applied soft computing 2015-03, Vol.28, p.160-166
• Main Authors: Kato, Yuichi, Saeki, Tetsuro, Mizuno, Shoutaro
• Format: Article
• Language: English
• Subjects: p-Value; Rough sets; Rule box; Rule induction; Statistical test
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2014.11.041

[Display omitted] •We propose a new rule induction method which drastically improves the method called LEM2 proposed by Jerzy Grzymala-Busse.•The new rule induction method named STRIM statistically and directly inducts if-then rules without using the concept of approximation by the conventional method.•The rules inducted by STRIM have statistical assurance of the confident coefficient of the p-value, and derive accuracy and coverage indexes used in the conventional method as by-products.•An algorithm for STRIM described in C language style is developed into a piece of software, implemented in a PC and confirmed to be efficient and useful for the rule induction problem by a simulation experiment. Rough sets theory is widely used as a method for estimating and/or inducing the knowledge structure of if-then rules from various decision tables. This paper presents the results of a retest of rough set rule induction ability by the use of simulation data sets. The conventional method has two main problems: firstly the diversification of the estimated rules, and secondly the strong dependence of the estimated rules on the data set sampling from the population. We here propose a new rule induction method based on the view that the rules existing in their population cause partiality of the distribution of the decision attribute values. This partiality can be utilized to detect the rules by use of a statistical test. The proposed new method is applied to the simulation data sets. The results show the method is valid and has clear advantages, as it overcomes the above problems inherent in the conventional method.