关键词: 不平衡数据集, 变异系数, SMOTE算法, 欠采样

Abstract: Aiming to solve the poor performance of imbalanced datasets classification, a novel imbalanced datasets classification algorithm based on mixed sampling（BSI） is proposed. This method firstly introduces coefficient of variation to find out the sparse domain and dense domain samples, and then deals with them in different ways, an oversampling method（BSMOTE） is proposed to improve the SMOTE algorithm for the minority samples in sparse domain. An improved undersampling method（IS） is proposed for the majority samples in dense domain. Finally, experiments on six imbalanced datasets show that the algorithm achieves higher G-mean value, F-value value, AUC value, and improves the comprehensive performance of imbalanced datasets classification effectively.

Key words: imbalanced datasets, coefficient of variation, SMOTE algorithm, undersampling