Web Classification Using Support Vector Machine
Conference Proceeding Article
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.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
WIDM '02: Proceedings of the 4th international workshop on Web information and data management
City or Country
McNeal, Virginia, USA
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. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/969