A data mining method for imbalanced datasets based on one-sided link and distribution density of instances

Classification in imbalanced datasets poses a great challenge to machine learning region, where the synthetic minority over-sampling technique (SMOTE) has become a powerful means and widely adopted as an effective method. But in generating new instances, SMOTE uses all instances in minority class such that it takes with over-generalization. To better solve the problem, a data mining method for imbalanced datasets based on one-sided link and distribution density of the minority (OSLDD-SMOTE) is proposed in this paper. OSLDD-SMOTE firstly selects the minority near the classification boundary using the one-sided link, then generates new instances with SMOTE based on the dynamic distribution density of these instances. Effects of synthetic degree on new generated instances and accuracy of the minority are respectively compared with the OSLDD-SMOTE, SMOTE, Borderline-SMOTE and Surrounding-SMOTE method. Furthermore, from the simulation results with 8 UCI datasets, our proposed method has the most accurate and robust performance on the G-mean, F-measure and AUC metrics.