关键词: 不平衡类, 少类样本合成过采样技术（SMOTE）, Bagging算法, 权重, 受试者工作特征曲线（ROC）

Abstract: The traditional Bagging method can achieve a high accuracy for imbalance data，but gets low accuracy of the minority class samples.In order to improve the accuracy of the minority class samples with Bagging algorithm，the paper proposes a two-step approach.Firstly，SMOTE algorithm is used to increase the number of the minority class samples and then adjusts the weight for each instance in Bagging according to its class value.Results of the confusing matrix and the ROC show the approach improves not only the classification performance of data as a whole but also that of the minority part.

Key words: imbalance dataset, Synthetic Minority Over-sampling Technique（SMOTE）, Bagging, weights, Receiver Operating Characteristic（ROC） curve