A New Method for Feature Selection

  • Yan Wu
  • Yang Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


We present a new approach based on discriminant analysis and regularization neural network for salient feature selection. Using the discriminant analysis based feature ranking, an ordered feature queue can be obtained according to the saliency of features. The neural network is trained by minimizing an augmented cross-entropy error function in the method. Feature selection is based on the reaction of the cross-validation data set classification error due to the removal of the individual features. The approach proposed is compared with four other feature selection methods, each of which banks on a different concept. The algorithm proposed outperforms the other methods by achieving higher classification accuracy on all the problems tested.


Feature Selection Feature Selection Method Correct Classification Rate Feature Selection Technique Fuzzy Entropy 
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

  • Yan Wu
    • 1
  • Yang Yang
    • 1
  1. 1.Department of Computer Science and EngineeringTongji UniversityShanghaiChina

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