An Empirical Evaluation of Repetitive Undersampling Techniques

Class imbalance is a fundamental problem in data mining and knowledge discovery which is encountered in a wide array of application domains. Random undersampling has been widely used to alleviate the harmful effects of imbalance, however, this technique often leads to a substantial amount of information loss. Repetitive undersampling techniques, which generate an ensemble of models, each trained on a different, undersampled subset of the training data, have been proposed to allieviate this difficulty. This work reviews three repetitive undersampling methods currently used to handle imbalance and presents a detailed and comprehensive empirical study using four different learners, four performance metrics and 15 datasets from various application domains. To our knowledge, this work is the most thorough study of repetitive undersampling techniques.