Feature Selection Using Non Linear Feature Relation Index

  • Namita Jain
  • C. A. Murthy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

In this paper we propose a dependence measure for a pair of features. This measure aims at identifying redundant features where the relationship between the features is characterized by higher degree polynomials. An algorithm is also proposed to make effective use of this dependence measure for the feature selection. Neither the calculation of dependence measure, nor the algorithm need the class values of the observations. So they can be used for clustering as well as classification.

Keywords

Feature Selection Dependence Measure Feature Subset Feature Selection Method Feature Selection Algorithm 
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.

References

  1. 1.
    Liu, H., Motoda, H.: Computational Methods of Feature Selection. Chapman Hall/CRCGoogle Scholar
  2. 2.
    Dash, M., Liu, H.: Feature Selection for Clustering. In: Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications, April 18-20, 2000, pp. 110–121 (2000)Google Scholar
  3. 3.
    Liu, H., Yu, L.: Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17(4), 491–502 (2005)CrossRefGoogle Scholar
  4. 4.
    Dash, M., Liu, H.: Feature Selection for Classification. Intelligent Data Analysis 1(3), 131–156 (1997)CrossRefGoogle Scholar
  5. 5.
    Devijver, P.A., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice Hall, Englewood Cliffs (1982)MATHGoogle Scholar
  6. 6.
    Hoel, P.G., Port, S.C., Stone, C.J.: Introduction to Statistical Theory. Houghton Mifflin, New York (1971)MATHGoogle Scholar
  7. 7.
    Mitra, P., Murthy, C.A., Pal, S.K.: Unsupervised Feature Selection Using Feature Similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3), 301–312 (2002)CrossRefGoogle Scholar
  8. 8.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository, http://www.ics.uci.edu/~mlearn/MLRepository.html
  9. 9.
    Pal, S.K., Mitra, P.: Patter Recognition Algorithms for Data Mining. ChapMan and Hall/CRCGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Namita Jain
    • 1
  • C. A. Murthy
    • 1
  1. 1.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia

Personalised recommendations