An empirical study of dynamic selection and random under-sampling for the class imbalance problem

A detailed and extensive empirical study of dynamic selection (DS) and random under-sampling (RUS) for the class imbalance problem is conducted in this paper. Total 20 state of the art DS methods are compared on 54 datasets. The empirical results clearly answer the following six key research questions in this direction, (1) how performances of ensembles with and without RUS compare with respect to different DS and static ensemble (SE) methods (2) how whether RUS is used affects performances of ensembles with respect to different DS/SE methods (3) how performances of different DS/SE methods compare with respect to ensembles with and without RUS (4) whether DS methods perform better than SE methods no matter whether RUS is used and what types of ensembles are used (5) how numbers of base classifiers affect how performances of ensembles with and without RUS compare with respect to different DS/SE methods (6) how numbers of base classifiers affect how performances of different DS/SE methods compare with respect to ensembles with and without RUS. The answers to the six research questions based on the experimental results in this study and the experimental findings are the main contributions of this work.