A Study on Fuzzy Classifier Design using MAP-Elites

Abstract

In pattern classification problems, it is becoming increasingly important to have a highly transparent classifier that enables us to understand the process and basis for classification. A fuzzy classifier has high transparency and can make decisions considering the uncertainties of the real world. Evolutionary computation has been actively used in fuzzy classifier design under the name of evolutionary fuzzy systems. MAP-Elites, an algorithm inspired by evolutionary computation, can search for optimal solutions while maintaining diversity in a predefined feature space. In this paper, we study fuzzy classifier design using MAP-Elites, which searches for accurate classifiers in the feature space based on the transparency-related complexity measures. We try to obtain a set of fuzzy classifiers with high accuracy and high diversity of transparency and further investigate the relationship between accuracy and transparency.