Abstract
The concept of rough sets, which was proposed by Z.Pawlak in 1982,can be employed to discuss the consistency of the classification given by human experts with the observed attribute values of each sample. Z.Pawlak proposed a method of reducing attributes, i.e., removing redundant attributes based on the equivalence relation defined by the attribute values of each sample. Tanaka et al. modified the Pawlak's method to remove more attributes by considering the given classification. They also constructed a fuzzy expert system based on the reduced attributes and applied it to a diagnosis problem of liver troubles. This paper aims to improve the performance of their fuzzy expert system by proposing a new fuzzy inference method. While only the most fitting fuzzy rule is usually used in their method, we take account of all fuzzy rules for classifying unknown samples. In order to consider all fuzzy rules, we use the average-product combination in classification process. The classification power of the proposed method is demonstrated by an application to the same diagnosis problem as in Tanaka et al.
