On the Use of Bagging, Mutual Information-Based Feature Selection and Multicriteria Genetic Algorithms to Design Fuzzy Rule-Based Classification Ensembles

In this contribution we explore the combination of bagging with random subspace and two variants of Battiti´s mutual information feature selection methods to design fuzzy rule-based classification system ensembles. Besides, we consider a multicriteria genetic algorithm guided by the training error to select the component classifiers, in order to look for appropriate accuracy-complexity trade-offs in the final multiclassifier.