基于样本特性欠取样的不均衡支持向量机

Support vector machine for unbalanced data based on sample properties under-sampling approaches

针对传统支持向量机在数据失衡的情况下分类效果很不理想的问题,提出一种基于样本特性欠取样的不均衡SVM分类算法.该算法首先在核空间中依据样本信息量选择一定比例的靠近不均衡分类界面的多数类样本;然后根据样本密度信息选择最具有代表性的均衡多数类样本点,在减少多数类样本的同时使分类界面向多数类方向偏移.实验结果表明,所提出的算法与其他不均衡数据预处理方法相比,能有效提高SVM算法在失衡数据中少数类的分类性能、总体分类性能和鲁棒性.

The classification result of classical support vector machine algorithm in the case of unbalanced data set is not satisfactory.Therefore,a under-sampling algorithm based on sample properties is presented.According to sample information in the kernel space,a certain percentage of majority instances located near the classification interface are selected.Then according to the sample’s density,the representive majority samples in the selected samples are selected,which can not only reduce the number of majority instances,but also make the SVM classification interface bias toward the majority instances.The experimental results show that compared with other data-preprocess methods for unbalanced dataset classification,the proposed method can improve the classification performance of SVM in the minority class data,the overall classification performance and robustness.