¹School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
²School of Information and Computer Engineering, North-East Forestry University, Harbin, China

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Abstract—Applying relational tri-training (R-tri-training for short) to web page classification is investigated in this paper. R-tri-training, as a new relational semi-supervised learning algorithm, is well suitable for learning in web page classification. The semi-supervised component of R-tritraining allows it to exploit unlabeled web pages to enhance the learning performance effectively. In addition, the relational component of R-tri-training is able to describe how the neighboring web pages are related to each other by hyperlinks. Experiments on Web-Kb dataset show that: 1) a large amount of unlabeled web pages (the unlabeled data) can be used by R-tri-training to enhance the performance of the learned hypothesis; 2) the performance of R-tri-training is better than the other algorithms compared with it.

Index Terms—web page classification, relational tri-training, relational learning, tri-training, co-training

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Cite: Yanjuan Li, Maozu Guo, "Web Page Classification Using Relational Learning Algorithm and Unlabeled Data," Journal of Computers vol. 6, no. 3, pp. 474-479, 2011.