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Abstract
The main objective of subgroup discovery is to discover interesting and interpretable patterns with respect to a specific property. The use of evolutionary fuzzy systems provides good algorithms to approach this problem. In this sense, NMEEF-SD algorithm –one of the most representative evolutionary fuzzy systems for subgroup discovery–obtains precise and interpretable subgroups. However in the majority of the evolutionary fuzzy systems, the membership functions of the linguistic labels are usually fixed to static values and the partitions are not adapted to the context of each variable. In this paper, a post-processing tuning step to improve the results of the subgroup discovery algorithm NMEEF-SD is proposed, allowing the partitions to be adapted to the context the variables. The application of this tuning step is a novelty in subgroup discovery and consist of a genetic algorithm which allows the lateral displacement of the membership functions of a label considering a unique parameter, using the 2-tuples linguistic representation. The results obtained using different data sets of the KEEL repository show the improvement in the performance of the NMEEF-SD algorithm with lateral displacement. The study is supported by statistical tests to improve the analysis performed.
