基于支持向量机的不平衡数据分类算法的研究

摘要

In view of the classification of the imbalance date set, this paper gave the method using SMOTE and modify kernel. First, used SMOTE method processing data, to reduce the imbalance. Then, used the conformal transformation and Rieman-nian metric to modify kernel, and reconstructed a new SVM with the modified kernel. Finally, used the new SVM to process the new data. Experimental results show that this method can improve the accuracy of the class with less training data under a high total accuracy.%针对不平衡数据分类问题,提出了基于Smote与核函数修改相结合的算法.首先用Smote方法处理数据,降低不平衡度;然后以黎曼几何为依据,利用保角变换,对核函数进行修改,提高支持向量机的分类泛化能力;最后用修改后的支持向量机对新的数据进行处理.实验结果表明,这种方法能在保持整体正确率的前提下有效地提高少数类样本的分类准确率.