Studying the behavior of a multiobjective genetic algorithm to design fuzzy rule-based classification systems for imbalanced data-sets

This paper studies the behavior of a multiobjective Genetic Algorithm for jointly performing a feature selection and granularity learning for Fuzzy Rule-Based Classification Systems in the scenario of imbalanced data-sets. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. We consider two different measures, one for the precision of the model and other for its complexity as the two objectives to optimize. In one previous approach, we aggregate these two measures in a single-objective Genetic Algorithm, and thus, a multiobjective approach of that Genetic Algorithm would yield a set of models with different trade-off between high accuracy and low complexity rather than a unique model, provided by the single-objective Genetic Algorithm. The experimental analysis, carried out over a wide range of imbalanced data-sets, shows that our approach is able to obtain a set of models with good trade-off between the two objectives considered but it is an open problem how to select the solution with best prediction ability from the whole set of solutions obtained.