Advertisement

Hybrid Feature Selection Method for Supervised Classification Based on Laplacian Score Ranking

  • Saúl Solorio-Fernández
  • J. Ariel Carrasco-Ochoa
  • José Fco. Martínez-Trinidad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)

Abstract

In this paper, we introduce a new hybrid filter-wrapper method for supervised feature selection, based on the Laplacian Score ranking combined with a wrapper strategy. We propose to rank features with the Laplacian Score to reduce the search space, and then we use this order to find the best feature subset. We compare our method against other based on ranking feature selection methods, namely, Information Gain Attribute Ranking, Relief, Correlation-based Feature Selection, and additionally we include in our comparison a Wrapper Subset Evaluation method. Empirical results over ten real-world datasets from the UCI repository show that our hybrid method is competitive and outperforms in most of the cases to the other feature selection methods used in our experiments.

Keywords

Supervised Feature Selection Laplacian Score Feature Ranking 

References

  1. 1.
    García, D.G., Rodríguez, R.S.: Spectral clustering and feature selection for microarray data. In: Fourth International Conference on Machine Learning and Applications, pp. 425–428 (2009)Google Scholar
  2. 2.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)Google Scholar
  3. 3.
    Liu, R., Yang, N., Ding, X., Ma, L.: An unsupervised feature selection algorithm: Laplacian Score combined with distance-based entropy measure. In: Workshop on Intelligent Information Technology Applications, vol. 3, pp. 65–68 (2009)Google Scholar
  4. 4.
    Niijima, S., Okuno, Y.: Laplacian linear discriminant analysis approach to unsupervised feature selection. IEEE/ACM Transactions on Computational Biology and Bioinformatics 6(4), 605–614 (2009)CrossRefGoogle Scholar
  5. 5.
    Jensen, R., Shen, Q.: Computational intelligence and feature selection: rough and fuzzy approaches, pp. 61–84. Wiley, Chichester (2008)CrossRefGoogle Scholar
  6. 6.
    von Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17(4), 395–416 (2007)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Zhao, Z., Liu, H.: Spectral feature selection for supervised and unsupervised learning. In: ICML ’07: Proceedings of the 24th International Conference on Machine learning, pp. 1151–1157. ACM, New York (2007)CrossRefGoogle Scholar
  8. 8.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. School of Information and Computer Science. University of California, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html Google Scholar
  9. 9.
    He, X., Cai, D., Niyogi, P.: Laplacian Score for feature selection. In: Weiss, Y., Schölkopf, B., Platt, J. (eds.) Advances in Neural Information Processing Systems, vol. 18, pp. 507–514. MIT Press, Cambridge (2006)Google Scholar
  10. 10.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  11. 11.
    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)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Loughrey, J., Cunningham, P.: Using Early-Stopping to Avoid Overfitting in Wrapper-Based Feature Selection Employing Stochastic Search. Technical Report (TCD-CS-2005-37). Department of Computer Science, Trinity College Dublin, Dublin, Ireland (2005)Google Scholar
  13. 13.
    Pal, S.K., Mitra, P.: Pattern Recognition Algorithms for Data Mining, pp. 59–82. Chapman & Hall/CRC (2004)Google Scholar
  14. 14.
    Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5, 1205–1224 (2004)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Zhang, L., Sun, G., Guo, J.: Feature selection for pattern classification problems. In: International Conference on Computer and Information Technology, pp. 233–237 (2004)Google Scholar
  16. 16.
    Guyon, I.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)zbMATHGoogle Scholar
  17. 17.
    Kim, Y.S., Nick Street, W., Menczer, F.: Feature selection in data mining, pp. 80–105 (2003)Google Scholar
  18. 18.
    Hall, M.A., Holmes, G.: Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering 15, 1437–1447 (2003)CrossRefGoogle Scholar
  19. 19.
    Nadeau, C., Bengio, Y.: Inference for the generalization error. Mach. Learn. 52(3), 239–281 (2003)CrossRefzbMATHGoogle Scholar
  20. 20.
    Xing, E.P., Jordan, M.I., Karp, R.M.: Feature selection for high-dimensional genomic microarray data. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 601–608 (2001)Google Scholar
  21. 21.
    Das, S.: Filters, wrappers and a boosting-based hybrid for feature selection. In: ICML ’01: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 74–81. Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
  22. 22.
    Dumais, S., Platt, J., Heckerman, D., Sahami, M.: Inductive learning algorithms and representations for text categorization. In: Proceedings of the International Conference on Information and Knowledge Management, pp. 148–155 (1998)Google Scholar
  23. 23.
    Hall, M.A.: Correlation-based feature selection for machine learning. PhD thesis, Department of Computer Science, University ofWaikato, Hamilton, New Zealand (1998)Google Scholar
  24. 24.
    Dash, M., Liu, H.: Hybrid search of feature subsets. In: PRICAI’98: Topics in Artificial Intelligence, pp. 238–249 (1998)Google Scholar
  25. 25.
    Dash, M., Liu, H.: Feature selection for classification. Intelligent Data Analysis 1, 131–156 (1997)CrossRefGoogle Scholar
  26. 26.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97, 273–324 (1997)CrossRefzbMATHGoogle Scholar
  27. 27.
    John, G.H., Langley, P.: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, pp. 338–345 (1995)Google Scholar
  28. 28.
    Kononenko, I.: Estimating attributes: Analysis and extensions of relief. In: Proceedings of the Seventh European Conference on Machine Learning, pp. 171–182. Springer, Heidelberg (1994)Google Scholar
  29. 29.
    Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)Google Scholar
  30. 30.
    Kira, K., Rendell, L.: A practical approach to feature selection. In: Proceedings of the Ninth International Conference on Machine Learning, pp. 249–256. Morgan Kaufmann, San Francisco (1992)Google Scholar
  31. 31.
    Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Saúl Solorio-Fernández
    • 1
  • J. Ariel Carrasco-Ochoa
    • 1
  • José Fco. Martínez-Trinidad
    • 1
  1. 1.National Institute for Astrophysics, Optics and ElectronicsSanta María TonantzintlaMexico

Personalised recommendations