Evolutionary learning by a sensitivity-accuracy approach for multi-class problems

Performance evaluation is decisive when improving classifiers. Accuracy alone is insufficient because it cannot capture the myriad of contributing factors differentiating the performances of two different classifiers and approaches based on a multi-objective perspective are hindered by the growing of the Pareto optimal front as the number of classes increases. This paper proposes a new approach to deal with multi-class problems based on the accuracy (C) and minimum sensitivity (S) given by the lowest percentage of examples correctly predicted to belong to each class. From this perspective, we compare different fitness functions (accuracy, C , entropy, E , sensitivity, S , and area, A ) in an evolutionary scheme. We also present a two stage evolutionary algorithm with two sequential fitness functions, the entropy for the first step and the area for the second step. This methodology is applied to solve six benchmark classification problems. The two-stage approach obtains promising results and achieves a high classification rate level in the global dataset with an acceptable level of accuracy for each class.