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MEFC: MAP-Elites-based Fuzzy Classifier Design

Takeru Konishi,Naoki Masuyama,Yusuke Nojima

Published 2025 in IEEE International Conference on Fuzzy Systems

ABSTRACT

Recently, the importance of highly transparent Artificial Intelligence (AI) has been increasing because there are many real-world applications in which the basis and internal0020process of making a decision are required when using AI. A fuzzy classifier is a highly transparent AI that can be interpreted linguistically and can make decisions considering real-world uncertainties. Multi-objective evolutionary algorithms have been actively used in fuzzy classifier design under the name of multi-objective evolutionary fuzzy systems. However, there is a possibility that multi-objective evolutionary algorithms converge to a few locally optimal solutions prematurely. Therefore, we propose a fuzzy classifier design method based on a quality diversity algorithm, which can improve diversity as well as performance. MAPElites, one of the most representative quality diversity algorithms, searches for optimal solutions while improving diversity in a predefined feature space. We show the usefulness of the proposed method, MAP-Elites-based Fuzzy Classifier design (MEFC), by comparing it with the fuzzy classifier design method based on an evolutionary multi-objective optimization algorithm. In this study, we use MEFC, which searches for accurate models in the feature space based on interpretability measures. In addition, we propose two-stage MEFC composed of the exploration stage and the exploitation stage. We show its usefulness by comparing it with the default version of MEFC.

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