Multi-objective evolutionary rule and condition selection for designing fuzzy rule-based classifiers

In this paper, we exploit a multi-objective evolutionary algorithm (MOEA) to generate fuzzy rule-based classifiers (FRBCs) with different trade-offs between classification accuracy and rule base complexity. In order to learn the rule base we employ a rule and condition selection (RCS) approach which aims to select a reduced number of rules from a heuristically generated rule base and concurrently a reduced number of conditions for each selected rule. During the multi-objective evolutionary process, we generate the rule bases of the FRBCs by the RCS approach and concurrently learn the membership function parameters of the linguistic values used in the rules. The MOEA has been tested on fifteen classification benchmarks and compared with a similar technique proposed recently in the literature. We show how the FRBCs generated by our approach can achieve considerable accuracies, despite a low rule base complexity.