A SVM method for web page categorization based on weight adjustment and boosting mechanism

Authors:
Mingyu Lu;Chonghui Guo;Jiantao Sun;Yuchang Lu
Affiliations:
,Institute of Computer Science and Technology, Dalian Maritime University, Dalian, China;Department of Computer Science and Technology, Tsinghua University, Beijing, China;Department of Computer Science and Technology, Tsinghua University, Beijing, China;Department of Computer Science and Technology, Tsinghua University, Beijing, China
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Year:
2005

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Abstract

Web page classification is an important research direction of web mining. In the paper, a SVM method of web page classification is presented. It include four steps: (1) using analysis module to extract the core text and structural tags from a web page; (2) adopting the improved VSM model to generate the initial feature vectors based on the core text of web page; (3) adjusting weights of the selected features based on structural tags in web page to generate the base SVM classifier; (4) combining the base classifiers produced by iteration based on Boosting mechanism to obtain the target SVM classifier. The experiment of web page classification shows that the approach presented is efficient.