A new rule induction method from a decision table using a statistical test

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.