Weighted Hyper-sphere SVM for Hypertext Classification

Authors:
Shuang Liu;Guoyou Shi
Affiliations:
College of Computer Science & Engineering, Dalian Nationalities University, Dalian, China 116600;College of Navigation, Dalian Maritime University, Dalian, China 116026
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

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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 tfidfresult 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.