文章基本信息

标题：Web Page Classification Using Relational Learning Algorithm and Unlabeled Data
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作者：Li, Yanjuan ; Guo, Maozu
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2011
卷号：6
期号：3
页码：474-479
DOI：10.4304/jcp.6.3.474-479
语种：English
出版社：Academy Publisher
摘要：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-tri-training 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.
关键词：web page classification;relational tri-training;relational learning;tri-training;co-training