An editor labeling model for training set expansion in Web categorization

Automatically classifying Web pages is an effective way to manage the massive information on the Web. However, our experiments show that the state-of-the-art text categorization technologies can not achieve a satisfactory classification performance in this task. The major reason is the existence of large proportion of rare categories in Web taxonomies. The failure in such categories is simply because there is not enough information to train reliable classifiers. To tackle this problem, we propose to expand the training set of the rare categories, by simulating the labeling behavior of the human editors of Web directories. Experimental results show that in such a way, we achieved significant (relatively 93%) improvement in classification accuracy, which is highly encouraging for high performance Web classification.