A New Maximum-Relevance Criterion for Significant Gene Selection

  • Young Bun Kim
  • Jean Gao
  • Pawel Michalak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4146)


Gene (feature) selection has been an active research area in microarray analysis. Max-Relevance is one of the criteria which has been broadly used to find features largely correlated to the target class. However, most approximation methods for Max-Relevance do not consider joint effect of features on the target class. We propose a new Max-Relevance criterion which combines the collective impact of the most expressive features in Emerging Patterns (EPs) and some popular independent criteria such as t-test and symmetrical uncertainty. The main benefit of this criterion is that by capturing the joint effect of features using EPs algorithm, it finds the most discriminative features in a broader scope. Experiment results clearly demonstrate that our feature sets improve the class prediction comparing to other feature selections.


Support Vector Machine Feature Selection Gene Selection Feature Subset Target Class 
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 2006

Authors and Affiliations

  • Young Bun Kim
    • 1
  • Jean Gao
    • 1
  • Pawel Michalak
    • 2
  1. 1.Department of Computer Science and Engineering 
  2. 2.Department of BiologyThe University of TexasArlingtonUSA

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