基于谱聚类欠取样的不均衡数据SVM分类算法

提出一种基于谱聚类欠取样的不均衡数据支持向量机(SVM)分类算法.该算法首先在核空间中对多数类样本进行谱聚类；然后在每个聚类中根据聚类大小和该聚类与少数类样本间的距离,选择具有代表意义的信息点；最终实现训练样本间的数目均衡.实验中将该算法同其他不均衡数据预处理方法相比较,结果表明该算法不仅能有效提高SVM算法对少数类的分类性能,而且总体分类性能及运行效率都有明显提高.

SVM classifier for unbalanced data based on spectrum cluster-based under-sampling approaches

An under-sampling unbalanced dataset support vector machine(SVM) algorithm based on spectrum cluster is
presented. Majority instances are clustered by using spectrum cluster in kernel space for resampling reprentative samples
with cluster information. The number of selected samples in each cluster is dependent on the size of each cluster and the
distance of the cluster to the all minority instances, which can not only reduce the number of majority instances, but also the
SVM classification performance under unbalanced dataset is improved by using the proposed method. In the experiments, the
proposed approach is compared with other data-preprocess methods for unbalanced dataset classification. The experimental
results show that the proposed method can not only improve classification performance of SVM algorithm in the minority
class data, but also increase the overall classification performance and effectivity.