Web page classification is significantly different from traditional text classification because of the presence of some additional information, provided by the HTML structure and by the presence of hyperlinks. In this paper we analyze these peculiarities and try to exploit them for representing web pages in order to improve categorization accuracy. We conduct various experiments on a corpus of ...

In this paper, a novel filter-based approach is proposed using the PageRank algorithm to select the optimal subset of features as well as to compute their weights for web page classification. To evaluate the proposed approach multiple experiments are performed using accuracy score as the main criterion on four different datasets, namely WebKB, Reuters-R8, Reuters-R52, and 20NewsGroups. By analy...

Spam pages are designed to maliciously appear among the top search results by excessive usage of popular terms. Therefore, spam pages should be removed using an effective and efficient spam detection system. Previous methods for web spam classification used several features from various information sources (page contents, web graph, access logs, etc.) to detect web spam. In this paper, we follo...

Increasing with the number of users, the need for automatic classification techniques with good classification accuracy increases as search engines depend on previously classified web pages stored in classified directories to retrieve the relevant results. Preprocessing is the important step in web page classification problem as most of the web pages contain more irrelevant information than rel...

For solving the uncertain problem in the process of WEB page classification, a general fusion classification model and algorithm are proposed, which based on model theory of information fusion. In the model, the hidden classification information is extracted from the WEB page, pre-processed firstly, then the processed data are input into the fusion mode, which deals with the different data with...