Comparing two genetic overproduce-and-choose strategies for fuzzy rule-based multiclassification systems generated by bagging and mutual information-based feature selection
Óscar Cordón, Arnaud Quirin · International Journal of Hybrid Intelligent Systems · 2010
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 w e 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.