Automatic Web Page Classification by Combining Feature Selection Techniques and Lazy Learners

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 show that lazy learners are capable of solving the web page classification problem. Our experimental results show that lazy learners classify the web page with acceptable accuracy using optimum number of attributes and LBR classifies more accurately than LWL classifiers.