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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)

Abstract

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.

Keywords

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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References

  1. 1.
    Jain, A., Zongker, D.: Feature Selection: Evaluation, Application, and Small Sample Performance. IEEE Trans. On Pattern Recognition and Machine Intelligence 19(2), 153–158 (1997)CrossRefGoogle Scholar
  2. 2.
    Yang, J., Honavar, V.: Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems 13(2), 44–49 (1998)CrossRefGoogle Scholar
  3. 3.
    Setiono, R., Liu, H.: Neural-network Feature Selector. IEEE Trans. Neural Networks 8(3), 654–662 (1997)CrossRefGoogle Scholar
  4. 4.
    Bauer, K.W., Alsing, S.G., Greene, K.A.: Feature Screening Using Signal-to-noise Ratios. Neurocomputing 31(1), 29–44 (2000)CrossRefGoogle Scholar
  5. 5.
    De, R.K., Pal, N.R., Pal, S.K.: Feature Analysis: Neural Network and Fuzzy Set Theoretic Approaches. Pattern Recognition 30(10), 1579–1590 (1997)MATHCrossRefGoogle Scholar
  6. 6.
    Pal, S.K., De, R.K., Basak, J.: Unsupervised Feature Evaluation: a Neuro-fuzzy Approach. IEEE Trans. Neural Networks 11(2), 366–376 (2000)CrossRefGoogle Scholar
  7. 7.
    Verikas, A., Bacauskiene, M.: Feature Selection with Neural Networks. Pattern Recognition Letters 23(11), 1323–1335 (2002)MATHCrossRefGoogle Scholar

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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