Web page categorization using hierarchical headings structure

The goal of Web page categorization is to classify the Web documents into a certain number of predefined categories. The previous works in this area employed a large number of labeled training documents for supervised learning. The problem is that, it is difficult to create the labeled training documents. While it is easy to collect the unlabeled documents, it is not so easy to manually categorize them for creating training documents. Therefore, a new machine learning algorithm should be investigated to overcome these difficulties. We proposed a new algorithm called Iterative Cross-Training (ICT). The paper also present a new feature set which is the hierarchical structure of headings appearing in the Web page to enhance the classification performance. We found that the hierarchical structure of headings has a high impact and could enhance the classification performance.