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Feature Selection with Rough Sets for Web Page Classification

Summary: Feature Selection with Rough Sets for Web
Page Classification
Aijun An1
, Yanhui Huang2
, Xiangji Huang1
, and Nick Cercone3
York University, Toronto, Ontario, M3J 1P3, Canada
University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada
Dalhousie University, Halifax, Nova Scotia, B3H 1W5, Canada
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 se-
lection 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.
1 Introduction


Source: An, Aijun - Department of Computer Science, York University (Toronto)
Huang, Jimmy - School of Information Technology, York University (Toronto)


Collections: Computer Technologies and Information Sciences