关键词: 非平衡数据集, 分类, 支持度, 轮盘赌选择, 合成少数过采样技术（SMOTE）

Abstract: Based on analyzing the shortages of SMOTE（Synthetic Minority Over-sampling Technique）, an improved SMOTE （SSMOTE） is presented. The key of SSMOTE lies on leading the concept of support and roulette wheel selection into SMOTE and making full use of the heterogeneous nearest-neighbor distribution information to achieve the fine control of the synthesis quality and quantity to the minority class samples. SSMOTE and KNN（K-Nearest Neighbor） are combined to handle the classification problem on imbalanced datasets, and extensive experiments are conducted to compare SSMOTE and algorithms in pertinent literatures on the UCI datasets. The simulation results show SSMOTE promises prominent synthesis effect to the minority class samples, and brings better classification performance on imbalanced datasets with KNN.

Key words: imbalanced datasets, classification, support, roulette wheel selection, Synthetic Minority Over-sampling Technique（SMOTE）