Web classification using support vector machine Aixin Sun, Ee-Peng Lim, and Wee Keong Ng   View Paper (PDF)   Return to Web mining, tools, and performance evaluation 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. DiSC'03 © 2003 Association for Computing Machinery