A Fast Supervised Method of Feature Ranking and Selection for Pattern Classification

  • Suranjana Samanta
  • Sukhendu Das
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)


This paper describes a fast, non-parametric algorithm for feature ranking and selection for better classification accuracy. In real world cases, some of the features are noisy or redundant, which leads to the question - which features must be selected to obtain the best classification accuracy? We propose a supervised feature selection method, where features forming distinct class-wise distributions are given preference. Number of features selected for final classification is adaptive, but depends on the dataset used for training. We validate our proposed method by comparing with an existing method using real world datasets.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Suranjana Samanta
    • 1
  • Sukhendu Das
    • 1
  1. 1.Dept. of CSEIIT MadrasChennaiIndia

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