A New Oversampling Method Based on the Classification Contribution Degree
Zhenhao Jiang, Tingting Pan, Chao Zhang, Jie Yang
Abstract
Data imbalance is a thorny issue in machine learning. SMOTE is a famous oversampling method of imbalanced learning. However, it has some disadvantages such as sample overlapping, noise interference, and blindness of neighbor selection. In order to address these problems, we present a new oversampling method, OS-CCD, based on a new concept, the classification contribution degree. The classification contribution degree determines the number of synthetic samples generated by SMOTE for each positive sample. OS-CCD follows the spatial distribution characteristics of original samples on the class boundary, as well as avoids oversampling from noisy points. Experiments on twelve benchmark datasets demonstrate that OS-CCD outperforms six classical oversampling methods in terms of accuracy, F1-score, AUC, and ROC.