A study on rough set-aided feature selection for automatic web-page classification

Abstract

Due to the recent explosive increase of Web-pages on World Wide Web, it is now urgently required for portal sites like Yahoo! service having directory-style search engines to classify Web-pages into many categories automatically. This paper investigates how rough set theory can help select relevant features for Web-page classification. Our experimental results show that the combination of the rough set-aided feature selection method and the Support Vector Machine with the linear kernel is quite useful in practice to classify Web-pages into multiple categories because not only our experiments give acceptable accuracy but also the high dimensionality reduction is achieved without the need to search for a threshold for feature selection.