Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
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
1
York University, Toronto, Ontario, M3J 1P3, Canada
2
University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada
3
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