Fuzzy Partition and Input Selection by Genetic Algorithms for Designing Fuzzy Rule-Based Classification Systems

摘要

For function approximation using fuzzy if-then rules, Nomura et al. (1992) proposed a genetic algorithm-based method for adjusting the fuzzy partition of an input space. In this paper, we apply their method to pattern classification problems. We have already extended the coding method to the case where intervals and trapezoidal membership functions can be used for antecedent fuzzy sets (Ishibuchi & Murata, 1996). There are, however, two drawbacks in these methods. One is that the resolution of each axis on which the fuzzy partition is adjusted should be prespecified by a decision-maker. The other is that the number of fuzzy if-then rules exponentially increases as the number of attributes increases. To cope with these drawbacks, we propose a genetic algorithmbased fuzzy partition method that has the following advantages: 1. The resolution of each axis is determined by the histogram of training patterns. 2. The membership function is determined by the histogram and a genetic algorithm. 3. Input selection is also performed by the genetic algorithm. We show the effectiveness of the proposed method by computer simulations on iris data with 4 attributes and wine data with 13 attributes.