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A Voting Method for the Classification of Web Pages

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3 Author(s)
Fang, R. ; Sch. of Informatics, Manchester Univ. ; Mikroyannidis, A. ; Theodoulidis, B.

This paper discusses Web page classification using hypertext features such as the text included in the Web page, the title, headings, URL, and anchor text. Five different classification approaches based on SVM that use individual features or combinations are investigated on the LookSmart dataset. The initial experimental results have shown that combining the features improves the performance of the classifier and that some features such as title and headings can be very useful for certain tasks. On the basis of this analysis, we propose a voting method that further improves the performance compared with the individual classifiers

Published in:

Web Intelligence and Intelligent Agent Technology Workshops, 2006. WI-IAT 2006 Workshops. 2006 IEEE/WIC/ACM International Conference on

Date of Conference:

18-22 Dec. 2006

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