Enhancing Web Page Classification via Local Co-training

Abstract

In this paper we propose a new multi-view semi-supervised learning algorithm called Local Co-Training(LCT). The proposed algorithm employs a set of local models with vector outputs to model the relations among examples in a local region on each view, and iteratively refines the dominant local models (i.e. the local models related to the unlabeled examples chosen for enriching the training set) using unlabeled examples by the co-training process. Compared with previous co-training style algorithms, local co-training has two advantages: firstly, it has higher classification precision by introducing local learning; secondly, only the dominant local models need to be updated, which significantly decreases the computational load. Experiments on WebKB and Cora datasets demonstrate that LCT algorithm can effectively exploit unlabeled data to improve the performance of web page classification.