Using evolutionary multiobjective techniques for imbalanced classifcation data

Abstract

The aim of this paper is to study the use of Evolutionary Multiobjective Techniques to improve the performance of Neural Net- works (NN). In particular, we will focus on classi¯cation problems where classes are imbalanced. We propose an evolutionary multiobjective ap- proach where the accuracy rate of all the classes is optimized at the same time. Thus, all classes will be treated equally independently of their pres- ence in the training data set. The chromosome of the evolutionary algo- rithm encodes only the weights of the training patterns missclassi¯ed by the NN, instead of all the parameters of the NN as in other approaches. Results show that the multiobjective approach is able to consider all classes at the same time, disregarding to some extent their abundance in the training set or other biases that restrain some of the classes of being learned properly.