A novel inductive semi-supervised SVM with graph-based self-training

Authors:
ShengJun Cheng;QingCheng Huang;JiaFeng Liu;XiangLong Tang
Affiliations:
Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

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Abstract

In this paper, a novel inductive support vector machine for semi-supervised learning, named IS3VM, is proposed, which aims to improve SVM by bootstrapping unlabeled data with self-training. The SVM classifier is iteratively refined through the augmentation of the training set. An improved self-training method is given by employing neighborhood graph for guarantying the reliability of newly added training examples. In detail, in each iteration of the self-training process, the local cut edge weight statistic is used to help estimate whether a newly labeled example is reliable or not, and only the reliable self-labeled examples are used to enlarge the labeled training set. Experiments show that, the improved self-training is beneficial and the proposed IS3VM algorithm can effectively exploit unlabeled data to achieve better performance, and is comparable to the-state-of-the-art semi-supervised SVM.