Incorporating Game Theory in Feature Selection for Text Categorization

  • Nouman Azam
  • JingTao Yao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6743)


Feature selection remains as one of effective and efficient techniques in text categorization. Selecting important features is crucial for effective performance in case of high imbalance in data. We introduced a method which incorporates game theory to feature selection with the aim of dealing with high imbalance situations for text categorization. In particular, a game is formed between negative and positive categories to identify the suitability of features for their respective categories. Demonstrative example suggests that this method may be useful for feature selection in text categorization problems involving high imbalance.


Feature Selection Game Theory Text Categorization Feature Selection Algorithm Negative Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nouman Azam
    • 1
  • JingTao Yao
    • 1
  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

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