Hierarchical Classification of Web Pages Using Support Vector Machine

Authors:
Yi Wang;Zhiguo Gong
Affiliations:
Faculty of Science and Technology, University of Macau, Macao, PRC;Faculty of Science and Technology, University of Macau, Macao, PRC
Venue:
ICADL 08 Proceedings of the 11th International Conference on Asian Digital Libraries: Universal and Ubiquitous Access to Information
Year:
2008

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Abstract

In this paper, a novel method for web page hierarchical classification is addressed. In our approach, SVM is used as the basic algorithm to separate any two sub-categories under the same parent node. In order to alleviate the ill shift of SVM classifier caused by imbalanced training data, we try to combine the original SVM classifier with BEV algorithm to create classifier called VOTEM. Then, a web document is assigned to a sub-category based on voting from all category-to-category classifiers. This hierarchical classification algorithm starts its work from the top of the hierarchical tree downward recursively until it triggers a stop condition or reaches the leaf nodes. And our experiment reveals that proposed algorithm obtains better results.