Weighted Hyper-sphere SVM for Hypertext Classification

Abstract

With more and more hypertext documents available online, hypertext classification has become one popular research topic in information retrieval. Hyperlinks, HTML tags and category labels distributed over linked documents provide rich classification information. Integrating these information and content tfidf result as document feature vector, this paper proposes a new weighted hyper-sphere support vector machine for hypertext classification. Based on eliminating the influence of the uneven class sizes with weight factors, the new method solves multi-class classification with less computational complexity than binary support vector machines. Experiments on benchmark data set verify the efficiency and feasibility of our method.