Advertisement

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)

Abstract

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.

References

  1. 1.
    Guoa, B., Damper, R., Gunna, S.R., Nelsona, J.: A fast separability-based feature-selection method for high-dimensional remotely sensed image classification. Pattern Recognition 41, 1653–1662 (2007)CrossRefGoogle Scholar
  2. 2.
    Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering 17, 491–502 (2005)CrossRefGoogle Scholar
  3. 3.
    Sim, J., Wright, C.C.: The Kappa statistic in reliability studies: Use, interpretation, and sample size requirements. Physical Therapy 85, 257–268 (2005)Google Scholar
  4. 4.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Computer Science and Scientific Computing Series. Academic Press, London (1990)zbMATHGoogle Scholar
  5. 5.
    Asuncion, A., Newman, D.: UCI machine learning repository (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

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

Personalised recommendations