面向类别比例偏移的半监督支持向量机方法

Shifted Label Proportion Aware Semi-supervised Support Vector Machine

当未标记数据与有标记数据类别比例偏移较大时,半监督支持向量机性能不佳.基于此情况,文中提出面向类别比例偏移的半监督支持向量机方法.首先估计未标记数据类中心,然后对多个类别比例下的类中心进行最坏情况集成,从而提升半监督支持向量机的性能保障.实验表明,文中方法有效提升半监督支持向量机在类别比例偏移时的性能保障.

When the label proportion of unlabeled data is far away from that of labeled data, direct supervised support vector machine （ SVM ） with only labeled data outperforms semi-supervised SVM （ S^3VM ） with unlabeled data. Thus, a shifted label proportion aware S^3VM（fairS^3VM） is proposed. Specifically, the label mean of unlabeled data is firstly estimated. Then multiple label means corresponding to multiple label proportions are integrated under the worst-case scenario. Experimental results show that the performance guarantee of S3 VMs is effectively improved when the label proportion is shifted.