Equilibrating the recognition of the minority Class in the imbalance context

Abstract

In pattern recognition, it is well known that the classifier performance depends on the classification rule and the complexities presented in the data sets (such as class overlapping, class imbalance, outliers, high-dimensional data sets among others). In this way, the issue of class imbalance is exhibited when one class is less represented with respect to the other classes. If the classifier is trained with imbalanced data sets, the natural tendency is to recognize the samples included in the majority class, ignoring the minority classes. This situation is not desirable because in real problems it is necessary to recognize the minority class more without sacrificing the precision of the majority class. In this work we analyze the behaviour of four classifiers taking into a count a relative balance among the accuracy classes.