Automatic categorization is the only viable method to deal with the scaling problem of the World Wide Web. In this paper, we propose a Web page classifier based on an adaptation of k-Nearest Neighbor (k-NN) approach. To improve the performance of k-NN approach, we supplement k-NN approach with a feature selection method and a term-weighting scheme using markup tags, and reform document-document similarity measure used in vector space model. In our experiments on a Korean commercial Web directory, our proposed methods in k-NN approach for Web page classification improved the performance of classification.

Web-page classification is much more difficult than pure-text classification due to a large variety of noisy information embedded in Web pages. In this paper, we propose a new Web-page classification algorithm based on Web summarization for improving the accuracy. We first give empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web-page classification algorithms. We then propose a new Web summarization-based classification algorithm and evaluate it along with several other state-of-the-art text summarization algorithms on the LookSmart Web directory. Experimental results show that our proposed summarization-based classification algorithm achieves an approximately 8.8% improvement as compared to pure-text-based classification algorithm. We further introduce an ensemble classifier using the improved summarization algorithm and show that it achieves about 12.9% improvement over pure-text based methods.