Machine learning techniques for url classification with supervised feature selection and rejection framework

Abstract

Web page classification has become a challenging task due to the exponential newlinegrowth of the World Wide Web There is a great demand for URL newlinebased web page classification systems as the features are extracted only from newlineURLs thereby avoiding the need for downloading web pages and increasing the newlinespeed of classification However achieving high accuracy may not be possible newlineas URLs contain minimal information Neverthless URL based classifiers along newlinewith a rejection framework can be used as a first level filter in a multistage hierarchical newlineclassifier by extracting cheaper features derived only from URLs newline newline