An analysis of evolutionary algorithms with different types of fuzzy rules in subgroup discovery

The interpretability of the results obtained and the quality measures used both to extract and evaluate the rules are two key aspects of Subgroup Discovery. In this study, we analyse the influence of the type of rule used to extract knowledge in Subgroup Discovery, and the quality measures more adapted to the evolutionary algorithms for Subgroup Discovery developed so far. The adaptation of the NMEF-SD algorithm to extract disjunctive formal norm rules is also presented.