A New Rule Induction Method from Decision Table by Use of a Statistical Test
Shotaro Mizuno, Yuichi Kato, Tetsuro Saeki
Rough Sets theory is widely used as a method for estimating and/or inducing knowledge structure of if-then rules from various decision tables. This paper presents results of a retest of rough set rule induction ability by the use of simulation data sets. The conventional method has two main problems: the first is the accuracy of the estimated rules, and the second is 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 using 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.