A Feature Selection Method Using Hierarchical Clustering

  • Cheong Hee Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)

Abstract

Feature selection refers to a problem to select a subset of features which are most optimal for intended tasks. As one of well-known feature selection methods, clustering features into several groups and picking one feature from each group have been used for unsupervised feature selection. Since the purpose of clustering in feature selection is to select a feature from each group, the quality of the feature to be selected should be considered in the clustering process. In this paper, we propose a feature selection method using hierarchical clustering. A new similarity measure between two feature groups is defined by directly using the representative feature in each group. Experimental results show that our method can select good features even for supervised learning.

Keywords

Feature selection Hierarchical clustering Ward method 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 19, 153–157 (2000)CrossRefGoogle Scholar
  2. 2.
    King, B.: Step-wise clustering procedures. Journal of the American Statistical Association 62(317), 86–101 (1967)CrossRefGoogle Scholar
  3. 3.
    Krier, C., Francois, D., Rossi, F., Verleysen, M.: Feature clustering and mutual information for the selection of variables in spectral data. In: Proceedings of European Symposium on Artificial Neural Networks, Bruges, Belgium (2007)Google Scholar
  4. 4.
    Butterworth, R., Piatetsky-Shapiro, G.: On feature selection through clustering. In: Proceedings of the Fifth IEEE International Conference on Data Mining (2005)Google Scholar
  5. 5.
    He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: Proceedings of Advances in Neural Information Processing Systems, Vancouver, Canada (2005)Google Scholar
  6. 6.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley Interscience, New York (2001)MATHGoogle Scholar
  7. 7.
    Ward Jr., J.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58(301), 236–244 (1963)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Cheong Hee Park
    • 1
  1. 1.Dept. of Computer Science and EngineeringChungnam National UniversityYuseong-guKorea

Personalised recommendations