Performance evaluation of fuzzy rule-based classification systems obtainedby multi-objective genetic algorithms

In this paper, we examine the classification performance of fuzzy if-then r ules selected by a GA-based multi-objective rule selection method. This rul e selection method can be applied to high-dimensional pattern classificatio n problems with many continuous attributes by restricting the number of ant ecedent conditions of each candidate fuzzy if-then rule. As candidate rules , we only use fuzzy if-then rules with a small number of antecedent conditi ons. Thus it is easy for human users to understand each rule selected by ou r method. Our rule selection method has two objectives: to minimize the num ber of selected fuzzy if-then rules and to maximize the number of correctly classified patterns. In our multi-objective fuzzy rule selection problem, there exist several solutions (i.e., several rule sets) called "non-dominat ed solutions" because two conflicting objectives are considered. In this pa per, we examine the performance of our GA-based rule selection method by co mputer simulations on a real-world pattern classification problem with many continuous attributes. First we examine the classification performance of our method for training patterns by computer simulations. Next we examine t he generalization ability for test patterns. We show that a fuzzy rule-base d classification system with an appropriate number of rules has high genera lization ability. (C) 1998 Elsevier Science Ltd. All rights reserved.