关键词: 非平衡, 支持向量机（SVM）, 少数类样本过采样技术（SMOTE）, 分类超平面, 混合采样

Abstract: The over-sampling and under-sampling that are used to classify unbalanced datasets are common methods. However, the two methods either lead to over-fitting or lose important samples. Based on this insight, a hybrid sampling algorithm SVM_HS (hybrid sampling algorithm based on support vector machine) based on the classification hyperplane is proposed, to solve the problem of the classification hyperplane of the SVM algorithm moving to the minority class easily. Firstly, the algorithm uses the SVM algorithm to obtain the classification hyperplane, then deletes some samples in the majority class that are far away from the hyperplane and generates some new samples belonging to the minority class near the real boundary with the SMOTE (synthetic minority over-sampling technique) iteratively, finally makes the classification hyperplane closer to the real boundary slowly. Compared with other resampling methods, experimental results have shown that the F-value and G-mean of the proposed algorithm are improved.

Key words: imbalance, support vector machine (SVM), synthetic minority over-sampling technique (SMOTE), classification hyperplane, hybrid sampling