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Fast-Ensembles of Minimum Redundancy Feature Selection

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 373)

Abstract

Finding relevant subspaces in very high-dimensional data is a challenging task not only for microarray data. The selection of features is to enhance the classification performance, but on the other hand the feature selection must be stable, i.e., the set of features selected should not change when using different subsets of a population. ensemble methods have succeeded in the increase of stability and classification accuracy. However, their runtime prevents them from scaling up to real-world applications.We propose two methods which enhance correlation-based feature selection such that the stability of feature selection comes with little or even no extra runtime.We show the efficiency of the algorithms analytically and empirically on a wide range of datasets.

Keywords

Feature Selection Linear Discriminant Analysis Feature Subset Feature Selection Method Ensemble Method 
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.

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References

  1. 1.
    Bontempi, G., Meyer, P.E.: Causal filter selection in microarray data. In: Fürnkranz, J., Joachims, T. (eds.) Proc. the 27th Int. Conf. Machine Learning, Haifa, Israel, pp. 95–102. Omnipress, Madison (2010)Google Scholar
  2. 2.
    Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)CrossRefzbMATHGoogle Scholar
  4. 4.
    Ding, C.H.Q., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. In: Proc. the 2nd IEEE Comp. Society Bioinformatics Conf., Stanford, CA, pp. 523–529. IEEE Comp. Society, Los Alamitos (2003)Google Scholar
  5. 5.
    Fox, R.J., Dimmic, M.W.: A two-sample Bayesian t-test for microarray data. BMC Bioinformatics 7 (2006)Google Scholar
  6. 6.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Gulgezen, G., Cataltepe, Z., Yu, L.: Stable and accurate feature selection. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5781, pp. 455–468. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Langley, P. (ed.) Proc. the 17th Int. Conf. Machine Learning, Stanford, CA, pp. 359–366. Morgan Kaufmann, San Francisco (2000)Google Scholar
  9. 9.
    Han, Y., Yu, L.: A variance reduction framework for stable feature selection. In: Webb, G.I., Liu, B., Zhang, C., Gunopulos, D., Wu, X. (eds.) Proc. the 10th IEEE Int. Conf. Data Mining, Sydney, Australia, pp. 206–215. IEEE Computer Society, Los Alamitos (2010)CrossRefGoogle Scholar
  10. 10.
    Jurman, G., Merler, S., Barla, A., Paoli, S., Galea, A., Furlanello, C.: Algebraic stability indicators for ranked lists in molecular profiling. Bioinformatics 24, 258–264 (2008)CrossRefGoogle Scholar
  11. 11.
    Kalousis, A., Prados, J., Hilario, M.: Stability of feature selection algorithms: a study on high-dimensional spaces. Knowledge and Inf. Syst. 12, 95–116 (2007)CrossRefGoogle Scholar
  12. 12.
    Koh, J.L.Y., Li Lee, M., Hsu, W., Lam, K.-T.: Correlation-based detection of attribute outliers. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 164–175. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97, 273–324 (1997)CrossRefzbMATHGoogle Scholar
  14. 14.
    Kuncheva, L.I.: A stability index for feature selection. In: Devedzic, V. (ed.) IASTED Int. Conf. Artif. Intell. and Appl., Innsbruck, Austria, pp. 421–427. ACTA Press, Calgary (2007)Google Scholar
  15. 15.
    Michalak, K., Kwasnicka, H.: Correlation-based feature selection strategy in neural classification. In: Proc. the 6th Int. Conf. Intell. Syst. Design and Appl., Jinan, China, pp. 741–746. IEEE Comp. Society, Los Alamitos (2006)Google Scholar
  16. 16.
    Saeys, Y., Abeel, T., Van de Peer, Y.: Robust feature selection using ensemble feature selection techniques. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 313–325. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Tusher, V.G., Tibshirani, R., Chu, G.: Significance analysis of microarrays applied to the ionizing radiation response. Proc. the National Academy of Sciences of the United States of America  98, 5116–5121 (2001)Google Scholar
  18. 18.
    Vapnik, V.: Statistical learning theory. Wiley, Chichester (1998)zbMATHGoogle Scholar
  19. 19.
    Xu, X., Zhang, A.: Boost feature subset selection: A new gene selection algorithm for microarray dataset. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3992, pp. 670–677. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  20. 20.
    Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Machine Learning Research 5, 1205–1224 (2004)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Technische Universität DortmundGermany

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