Comparing two genetic overproduce-and-choose strategies for fuzzy rule-based multiclassification systems generated by bagging and mutual information-based feature selection

In [14] we proposed a scheme to generate fuzzy rule-based multiclassification systems by means of bagging, mutual information-based feature selection, and a multicriteria genetic algorithm for static component classifier selection guided by the ensemble training error. In the current contribution we extend the latter component by making use of the bagging approach's capability to evaluate the accuracy of the classifier ensemble using the out-of-bag estimates. An exhaustive study is developed on the potential of the two multicriteria genetic algorithms respectively considering the classical training error and the out-of-bag error fitness functions to design a final multiclassifier with an appropriate accuracy-complexity trade-off. Several parameter settings for the global approach are tested when applied to nine popular UCI datasets with different dimensionality.