Learning compact fuzzy rule-based classification systems with genetic programming

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 paper we propose a genetic- programming-based method, where the disjunctive normal form (DNF) fuzzy rules compete in order to obtain an FRBCS with high interpretability and accuracy. The good results obtained with several classification problems support our proposal.