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Rough Set-Aided Feature Selection for Automatic Web-Page Classification

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3 Author(s)
Wakaki, T. ; Shibaura Institute of Technology, Japan ; Itakura, H. ; Tamura, M.

Recently Web-pages on the World Wide Web are explosively increasing, and it is now required for portal sites such as Yahoo! service having directory-style search engines to classify Web-pages into many categories automatically. This paper investigates how rough settheory can help select relevant features for Web-page classification. Our experimental results show that the combination of the rough set-aided feature selection method and the Support Vector Machine with a linear kernel is quite useful in practice to classify Web-pages into many categories because not only the performance gives acceptable accuracy but also the high dimensionality reduction is achieved without depending on arbitrary thresholds for feature selection.

Published in:

Web Intelligence, 2004. WI 2004. Proceedings. IEEE/WIC/ACM International Conference on

Date of Conference:

20-24 Sept. 2004

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