Interpretability-based fuzzy decision tree classifier a hybrid of the subtractive clustering and the multi-objective evolutionary algorithm


The fuzzy decision tree (FDT) is a powerful, top-down, hierarchical search scheme to extract human interpretable classification rules. Furthermore, the FDT is considered an approach to model a system by making use of a descriptive language with fuzzy logic and fuzzy predicates. There exist two contradictory requirements with fuzzy modeling, namely, accuracy and interpretability. In this study, the subtractive clustering and a multi-objective evolutionary algorithm are used to develop a novel fuzzy modeling scheme based on the FDT classifier to construct an accurate and interpretable system that is defined as the interpretability-based fuzzy decision tree classifier. Two interpretability measures, namely, complexity interpretability and semantics interpretability are considered to reach acceptable accuracy. These measures are optimized as different objectives within the multi-objective framework. Results obtained in several benchmark classification problems are encouraging because they show the ability of the developed scheme while yielding accuracy comparable to that achieved by other methods like neural networks and the crisp decision tree (C4.5). The experimental results demonstrate the superiority of the developed scheme.