Genetic algorithm based fuzzy ID3 algorithm

Abstract

In this paper, we propose a genetic algorithm (GA) based fuzzy ID3 algorithm to construct a fuzzy classification system with both high classification accuracy and compact rule base size. This goal is achieved by two key steps. First, we optimize by GA the parameters controlling the means and variances of fuzzy membership functions and leaf node conditions for tree construction. Second, we prune the rules of the tree constructed by evaluating the effectiveness of the rule, and the remaining rules are retrained by the same GA proposed. Our proposed scheme is tested on various famous data sets, and its results is compared with C4.5 and IRID3. Simulation result shows that our proposed scheme leads to not only better classification accuracy but also smaller size of rule base.