A new approach to handle high dimensional and large datasets in multi-objective evolutionary fuzzy systems

In the framework of multi-objective evolutionary fuzzy systems (MOEFSs), the search space grows as the number of features of the dataset increases, leading to a slow and possibly difficult convergence of the evolutionary algorithm. Furthermore, mainly due to the fitness evaluation, datasets with a large number of instances require very high computational costs. In this paper, we propose a co-evolutionary approach to generate sets of Mamdani fuzzy rule-based systems (MFRBSs) with different trade-offs between accuracy and interpretability. We aim to deal with high dimensional and large datasets and to learn together the rule base (RB) and the membership function parameters. To reduce the search space, we perform the multi-objective evolutionary learning of the RB by selecting reduced sets of rules and conditions from a previously generated RB. Further, to lessen the computational costs, during the multi-objective evolutionary learning process, periodically, a single-objective genetic algorithm evolves a population of reduced training sets. We show the preliminary results obtained by applying our approach to two real world high dimensional and large regression datasets.