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文中针对不平衡数据导致分类结果倾斜现象,提出了一种结合SMOTE和GEPSVM的分类方法.该方法利用SMOTE过采样重构训练集,使训练集达到相对平衡,避免了重复样本数据带来的过学习问题,最后用GEPSVM进行分类学习.在UCI数据集上的实验证明了该算法在不平衡数据集上与传统的SVM算法相比有更好的分类效果,在计算时间上也有一定的优势.“,”In this paper,a GEPSVM algorithm based on SMOTE over-sampling method is proposed to address the problem of skewed classification results in classification algorithms.This algorithm utilizes the SMOTE over-sampling method to reconstruct training datasets.As a result,the training datasets are relatively balanced and the over-fitting problem caused by repeated sample data is avoided.Finally,it utilizes GEPSVM to conduct learning.The experiments on the UCI datasets demonstrate that the proposed algorithm achieves better classification results and requires shorter computation time than the traditional SVM algorithm on imbalanced datasets.