Abstract
Web page classification is the problem of assigning predefined categories to web pages. A challenge in web page classification is how to deal with the high dimensionality of the feature space. We present a feature reduction method based on the rough set theory and investigate the effectiveness of the rough set feature selection method on web page classification. Our experiments indicate that rough set feature selection can improve the predictive performance when the original feature set for representing web pages is large.
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© 2004 Springer-Verlag Berlin Heidelberg
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An, A., Huang, Y., Huang, X., Cercone, N. (2004). Feature Selection with Rough Sets for Web Page Classification. In: Peters, J.F., Skowron, A., Dubois, D., Grzymała-Busse, J.W., Inuiguchi, M., Polkowski, L. (eds) Transactions on Rough Sets II. Lecture Notes in Computer Science, vol 3135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27778-1_1
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DOI: https://doi.org/10.1007/978-3-540-27778-1_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23990-1
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