A Learning Approach with Under-and Over-Sampling for Imbalanced Data Sets

Abstract

It is difficult for learning models to achieve high classification performance with imbalanced data sets. To conquer the problem, this study presents a strategy involving the reduction of size of majority data set and the generation of synthetic samples of minority data set. Parkinson's disease data set is used to examine and to compare the performance of classification methods. The paired t-tests are also used to show the effectiveness of the proposed method compari.ng with that of the other methods.