Non-dominated Multi-objective Evolutionary Algorithm Based on Fuzzy Rules Extraction for Subgroup Discovery

A new multi-objective evolutionary model for subgroup discovery with fuzzy rules is presented in this paper. The method resolves subgroup discovery problems based on the hybridization between fuzzy logic and genetic algorithms, with the aim of extracting interesting, novel and interpretable fuzzy rules. To do so, the algorithm includes different mechanisms for improving diversity in the population. This proposal focuses on the classification of individuals in fronts, based on nondominated sort. A study can be seen for the proposal and other previous methods for different databases. In this study good results are obtained for subgroup discovery by this new evolutionary model in comparison with existing algorithms.