Learning Fuzzy Rules using Genetic Programming: Context-free grammar definition for high-dimensionality problems

The inductive learning of a fuzzy rule-based classification system (FRBCS) with high interpretability is made difficult by the presence of a large number of features that increases the dimensionality of the problem being solved. The difficult comes from the exponential growth of the fuzzy rule search space with the increase in the number of features considered. In this work we tackle this problem, the FRBCS learning with high interpretability for high-dimensionality problems. We propose a genetic-programming-based method, where the evolved disjunctive normal form fuzzy rules compete in order to obtain an FRBCS with high interpretability (few rules and few antecedent conditions per rule) while maintaining a good performance.