A New Oversampling-Based Approach for Class Imbalance Problem

Abstract

In this paper, a new oversampling method is proposed to improve the representativeness of minority groups in the training data set. Our methodology creates artificial (synthetic) examples on basis the spatial distribution of the classes. The original data are expanded (duplicated) along the lines connecting the class centroid and each minority pattern under consideration. In contrast to other methods known in literature (as SMOTE), our geometric approach for data generation has the advantage of being accomplished in a straightforward way, i.e., without the need of the definition of parameters by the user. Experiments conducted with real and synthetic data point out that the our solution to the class imbalance problem is able to improve the number of correct minority classifications and the balance between the class accuracies.