An empirical study of oversampling and undersampling for instance selection methods on imbalance datasets

Abstract

Instance selection methods get low accuracy in problems with imbalanced databases. In the literature, the problem of imbalanced databases has been tackled applying oversampling or undersampling methods. Therefore, in this paper, we present an empirical study about the use of oversampling and undersampling methods to improve the accuracy of instance selection methods on imbalanced databases. We apply different oversampling and undersampling methods jointly with instance selectors over several public imbalanced databases. Our experimental results show that using oversampling and undersampling methods significantly improves the accuracy for the minority class. © Springer-Verlag 2013.