Publication Type
Conference Proceeding Article
Version
publishedVersion
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
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
Citation
SUN, Aixin and LIM, Ee Peng.
Web classification using Support Vector Machine. (2002). WIDM '02: Proceedings of the 4th international workshop on Web information and data management. 96-99.
Available at: https://ink.library.smu.edu.sg/sis_research/969
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1145/584931.584952
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons