Using fuzzy formal concepts in the genetic generation of fuzzy systems

Fuzzy classification systems have been widely researched in the literature. Genetic fuzzy systems combine the power of the global search of genetic algorithms with fuzzy systems to provide accurate and interpretable rule-based systems. In this paper, we present a new approach for the genetic generation of fuzzy systems. The novelty of our proposal, named FCA-Based method, is a hybrid combination of fuzzy formal concepts to extract rules to form the search space of a genetic algorithm. FCA-Based extracts rules from existing data using the fuzzy formal concept analysis theory. FCA-Based uses the set of a priori extracted rules to form the final fuzzy rule bases by means of its genetic process. FCA-Based was tested using 10 datasets and a 10-fold cross-validation strategy using 4 different fuzzy data bases. The main comparisons included in this work are related to the number of extracted rules forming the genetic search spaces between FCA-BASED and DOC-BASED. Results are then analysed according to their accuracy and intepretability. The obtained results are adequate for the tested datasets.