Use of Pareto-Optimal and Near Pareto-Optimal Candidate Rules in Genetic Fuzzy Rule Selection

Abstract

Genetic fuzzy rule selection is an effective approach to the design of accurate and interpretable fuzzy rule-based classifiers. It tries to minimize the complexity of fuzzy rule-based classifiers while maximizing their accuracy by selecting only a small number of fuzzy rules from a large number of candidate rules. One important issue in genetic fuzzy rule selection is the prescreening of candidate rules. If we use too many candidate rules, a large computation load is required to search for good rule sets. On the other hand, good rule sets will not be found when promising fuzzy rules are not included in the candidate rule set. It is essential for the success of genetic fuzzy rule selection to use a tractable number of promising fuzzy rules as candidate rules. In this paper, we propose an idea of using Pareto-optimal and near Pareto-optimal fuzzy rules as candidate rules in genetic fuzzy rule selection. Pareto-optimality is defined by two well-known data mining criteria: support and confidence. To extract not only Paretooptimal but also near Pareto-optimal fuzzy rules, we modify Pareto dominance using a dominance margin e . Through computational experiments, we examine the effect of the proposed idea on multiobjective genetic fuzzy rule selection.