Title

Web Classification Using Support Vector Machine

Publication Type

Conference Proceeding Article

Publication Date

11-2002

Abstract

In web classification, web pages from one or more web sites are assigned to pre-defined categories according to their content. Since web pages are more than just plain text documents, web classification methods have to consider using other context features of web pages, such as hyperlinks and HTML tags. In this paper, we propose the use of Support Vector Machine (SVM) classifiers to classify web pages using both their text and context feature sets. We have experimented our web classification method on the WebKB data set. Compared with earlier Foil-Pilfs method on the same data set, our method has been shown to perform very well. We have also shown that the use of context features especially hyperlinks can improve the classification performance significantly.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

WIDM '02: Proceedings of the 4th international workshop on Web information and data management

First Page

96

Last Page

99

ISBN

9781581135930

Identifier

10.1145/584931.584952

Publisher

ACM

City or Country

McNeal, Virginia, USA

Additional URL

http://dx.doi.org/10.1145/584931.584952

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