Combined Feature Selection and classification – A novel approach for the categorization of web pages

Abstract. The World Wide Web is growing rapidly and the Internet users are still increasing day by day. 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 as classified directories to retrieve the relevant results. Machine learning techniques for automatic classification gains more interest as the classifier improves its performance with experience. In this paper we propose a method called Combined Feature Selection and Classification for effective categorization of web pages. Our experimental results show that our proposed approach improves the classification accuracy with the optimum number of attributes. We experimented with four machine learning classifiers (CV Parameter Selection, Logit Boost, Random Committee and VFI).Our results effectively improve the accuracy. 1