Multilabel text categorization based on a new linear classifier learning method and a category-sensitive refinement method

  • Authors:
  • Yu-Chuan Chang;Shyi-Ming Chen;Churn-Jung Liau

  • Affiliations:
  • Department of Computer Science and Information Engineering, National Taiwan, University of Science and Technology, 43, Section 4, Keelung Road, Taipei 106, Taiwan, ROC;Department of Computer Science and Information Engineering, National Taiwan, University of Science and Technology, 43, Section 4, Keelung Road, Taipei 106, Taiwan, ROC and Department of Computer S ...;Institute of Information Science, Academia Sinica, Taipei, Taiwan, ROC

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2008

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Abstract

In this paper, we present a new approach for dealing with multilabel text categorization based on a new linear classifier learning method and a category-sensitive refinement method. We use a new weighted indexing technique to construct a multilabel linear classifier. We use the degrees of similarity between categories to adjust the relevance scores of categories with respect to a testing document. The testing document can be properly classified into multiple categories by using a predefined threshold value. We also compare the performance of the proposed method with the text categorization methods based on the Reuters-21578 ModeApte Split Text Collection. The experimental results show that the performance of the proposed method is better than the existing methods.