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Multilabel text categorization based on a new linear classifier learning method and a category-sensitive refinement method
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DOI：10.1016/j.eswa.2007.02.037.png)
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Abstract
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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. (c) 2007 Elsevier Ltd. All rights reserved.
Keywords:
text categorization
text classifiers
category-sensitive refinement method
multilabel text categorization
relevance scores
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