基于改进smote的非平衡数据集分类研究_王超学.pdf

184 2013 ，49 （2 ） Computer Engineering and Applications 计算机工程与应用 基于改进SMOTE 的非平衡数据集分类研究 1 1 1 2 1 王超学 ，潘正茂 ，董丽丽 ，马春森 ，张 星 1 1 1 2 1 WANG Chaoxue , PAN Zhengmao , DONG Lili , MA Chunsen , ZHANG Xing 1.西安建筑科技大学 信息与控制工程学院，西安 710055 2.中国农业科学院 植物保护研究所，北京 100193 1.School of Information and Control Engineering, Xi ’an University of Architecture and Technology, Xi ’an 710055, China 2.Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China WANG Chaoxue, PAN Zhengmao, DONG Lili, et al. Research on classification for imbalanced dataset based on improved SMOTE. Computer Engineering and Applications, 2013, 49 （2 ）：184-187. 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 classi- fication problem on imbalanced datasets, and extensive experiments are conducted to compare SSMOTE and algorithms in perti- nent 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） 摘 要：针对SMOTE （Synthetic Minority Over-sampling Technique ）在合成少数类新样本时存在的不足，提出了一种改进 的SMOTE 算法（SSMOTE ）。该算法的关键是将支持度概念和轮盘赌选择技术引入到SMOTE 中，并充分利用了异类近邻 的分布信息，实现了对少数类样本合成质量和数量的精细控制。将SSMOTE 与KNN （K -Neares