Skip to main content
SpringerLink
Log in

A filter-dominating hybrid sequential forward floating search method for feature subset selection in high-dimensional space

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Sequential forward floating search (SFFS) has been well recognized as one of the best feature selection methods. This paper proposes a filter-dominating hybrid SFFS method, aiming at high efficiency and insignificant accuracy sacrifice for high-dimensional feature subset selection. Experiments with this new hybrid approach have been conducted on five feature data sets, with different combinations of classifier and separability index as alternative criteria for evaluating the performance of potential feature subsets. The classifiers under consideration include linear discriminate analysis classifier, support vector machine, and K-nearest neighbors classifier, and the separability indexes include the Davies-Bouldin index and a mutual information based index. Experimental results have demonstrated the advantages and usefulness of the proposed method in high-dimensional feature subset selection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. The challenge itself contains EEG signals from 7 subjects, recorded during synchronous BCI experiments. Three subjects are synthesized, thus we used only the data recorded from 4 human subjects.

References

  1. Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502

    Article  Google Scholar 

  2. Gan JQ (2006) Feature dimensionality reduction by manifold learning in brain-computer interface design. In: 3rd international workshop on brain-computer interfaces. Graz, Austria, pp 28–29

  3. Gheyas IA, Smith LS (2010) Feature subset selection in large dimensionality domains. Pattern Recogn 43(1):5–13

    Article  MATH  Google Scholar 

  4. Awwad Shiekh Hasan B, Gan JQ, Zhang Q (2010) Multi-objective evolutionary methods for channel selection in brain-computer interfaces: some preliminary experimental results. In: IEEE congress on evolutionary computation. Barcelona, Spain, pp 3339–3344

  5. Tong DL, Mintram R (2010) Genetic algorithm-neural network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection. Int J Mach Learn Cybern 1(1–4):75–87

    Article  Google Scholar 

  6. Boehm O, Hardoon DR, Manevitz LM (2011) Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms. Int J Mach Learn Cybern 2(3):125–134

    Article  Google Scholar 

  7. Davies JL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1:224–227

    Article  Google Scholar 

  8. Bezdek JC, Pal NR (1998) Some new indexes of cluster validity. IEEE Trans SMC-Part B 28(3):301–315

    Google Scholar 

  9. Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Article  Google Scholar 

  10. Handl J, Knowles J (2006) Feature subset selection in unsupervised learning via multiobjective optimization. Int J Comput Intell Res 2(3):217–238

    MathSciNet  Google Scholar 

  11. Das S (2001) Filters, wrappers and a boosting-based hybrid for feature selection. In: Proceedings 18th international conference on machine learning, pp 74–81

  12. Sebban M, Nock R (2002) A hybrid filter/wrapper approach of feature selection using information theory. Pattern Recogn 35:835–846

    Article  MATH  Google Scholar 

  13. Somol P, Novovocova J, Pudil P (2006) Flexible-hybrid sequential floating search in statistical feature selection. In: Lecture notes in computer science, vol 4109. Springer, Berlin, pp 632–639

  14. Huang J, Cai Y, Xu X (2007) A hybrid genetic algorithm for feature selection wrapper based on mutual information. Pattern Recogn Lett 28(13):1825–1844

    Article  Google Scholar 

  15. Uncu O, Turksen IB (2007) A novel feature selection approach: combining feature wrappers and filters. Inf Sci 177:449–466

    Article  MATH  MathSciNet  Google Scholar 

  16. Bermejo P, Gamez JA, Puerta JM (2011) A GRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets. Pattern Recogn Lett 32(5):701–711

    Article  MathSciNet  Google Scholar 

  17. Pudil P, Novovicova J, Kittler J (1994) Floating search methods in feature selection. Pattern Recogn Lett 15:1119–1125

    Article  Google Scholar 

  18. Jain A, Zongker D (1997) Feature selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19(2):153–158

    Article  Google Scholar 

  19. Dyson M, Balli T, Gan JQ, Sepulveda F, Palaniappan R (2008) Approximate entropy for EEG-based movement detection. In: 4th international workshop on brain-computer interfaces, Graz, Austria, pp 110–115

  20. Blankertz B, Dornhege G, Krauledat M, Mller K, Curio G (2007) The non-invasive berlin brain-computer interface: fast acquisition of effective performance in untrained subjects. NeuroImage 37:539–550

    Google Scholar 

  21. Desmar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK

    John Q. Gan, Bashar Awwad Shiekh Hasan & Chun Sing Louis Tsui

Authors
  1. John Q. Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bashar Awwad Shiekh Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chun Sing Louis Tsui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to John Q. Gan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gan, J.Q., Awwad Shiekh Hasan, B. & Tsui, C.S.L. A filter-dominating hybrid sequential forward floating search method for feature subset selection in high-dimensional space. Int. J. Mach. Learn. & Cyber. 5, 413–423 (2014). https://doi.org/10.1007/s13042-012-0139-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-012-0139-z

Keywords

Search

Navigation