Learning Web Categorization with Controlled Generation of Context Features

Automatic categorization of Web pages is an important area of study due to the rapidly growing amount of Web data. Efficient and accurate classification would greatly facilitate finding what one needs in the sea of information. Context-sensitive techniques have been proven to be effective in the classification task. However, the feature space for context feature that one can explore in these techniques is enormous. To consider these features comprehensively often become prohibitive in terms of resource requirements. In this paper, we propose an approach to intelligently control generating context features for the classification learning process. We present our investigation of this approach in the context of Web page categorization using the sleeping-experts technique.