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

摘要： 传统的Bagging分类方法对不平衡数据集进行分类时，虽然能够达到很高的分类精度，但是对其中少数类的分类准确率不高。为提高其对少数类数据的分类精度，利用SMOTE算法对样例集中的少数类样例进行加工，在Bagging算法中根据类值对各个样例的权重进行调整。混淆矩阵和ROC曲线表明改进算法达到了既能保证整体的分类准确率，又能提高少数类分类精度的目的。

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）