改进Smote算法在不平衡数据集上的分类研究

在不平衡数据集中，过抽样算法如Smote(Synthetic Minority Oversampling)算法、R-Smote算法与SD-ISmote算法可能会模糊多数类与少数类的边界以及使用噪声数据合成新样本。本文提出的ImprovedSmote算法使用少数数据集的簇心与其对应类别的少数集数据，在簇心与不大于样本属性数的对应类别少数集数据形成的图形内随机插值来生成新数据。ImprovedSmote算法结合C4.5决策树与神经网络算法在实验数据集上的结果比Smote, R-Smote与SD-ISmote算法更好，可以有效地提高分类器分类性能。

Research on Classification of Improved Smote Algorithm on Imbalanced Datasets

In imbalanced datasets, the oversampling algorithm, such as Smote (Synthetic Minority Oversampling) algorithm, R-Smote algorithm and SD-ISmote algorithm, may blur the boundary between the majority and the minority and use noisy data to synthesize new samples. The ImprovedSmote algorithm proposed in this paper uses cluster center of minority set and their corresponding minority set to generate new samples. The Smote, the R-Smote, the SD-ISmote and the ImprovedSmote algorithm combined C4.5 decision tree and neural network algorithm are used on the experimental datasets. The results show that the ImprovedSmote algorithm is better than other algorithms in classification and can effectively improve classifier performance.