Abstract
This paper shows how a small number of fuzzy rules can be selected for designing interpretable fuzzy rule-based classification systems. Our approach consists of two phases: candidate rule generation by data mining criteria and rule selection by evolutionary multiobjective optimization (EMO) algorithms. First a large number of candidate rules are generated and prescreened using two rule evaluation criteria in data mining. Next a small number of fuzzy rules are selected from candidate rules using multiobjective genetic algorithms. Our rule selection is formulated as a combinatorial optimization problem with three objectives: To maximize the classification accuracy, to minimize the number of selected rules, and to minimize the total rule length. Thus the task of genetic algorithms is to find non-dominated rule sets with respect to the three objectives.
