Rank Aggregation for Filter Feature Selection in Credit Scoring

  • Waad Bouaguel
  • Ghazi Bel Mufti
  • Mohamed Limam
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)

Abstract

The credit industry is a fast growing field, credit institutions collect data about credit customer and use them to build credit model. The collected information may be full of unwanted and redundant features which may speed down the learning process, so, effective feature selection methods are needed for credit dataset. In general, Filter feature selection methods outperform other feature selection techniques because they are effective and computationally fast. Choosing the appropriate filtering method from the wide variety of classical filtering methods proposed in the literature is a crucial issue in machine learning. So, we propose a feature selection fusion model that fuses the results obtained by different filter feature selection methods via aggregation techniques. Evaluations on four credit datasets show that the fusion model achieves good results.

Keywords

Feature selection filter aggregation error curve 

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References

  1. 1.
    Wang, C.M., Huang, W.F.: Evolutionary-based feature selection approaches with new criteria for data mining: A case study of credit approval data. Expert Syst. Appl. 36, 5900–5908 (2009)CrossRefGoogle Scholar
  2. 2.
    Howley, T., Madden, M.G., O’Connell, M.L., Ryder, A.G.: The effect of principal component analysis on machine learning accuracy with high-dimensional spectral data. Knowl.-Based Syst. 19, 363–370 (2006)CrossRefGoogle Scholar
  3. 3.
    Guldogan, E., Gabbouj, M.: Feature selection for content-based image retrieval. Signal, Image and Video Processing, 241–250 (2008)Google Scholar
  4. 4.
    Rodriguez, I., Huerta, R., Elkan, C., Cruz, C.S.: Quadratic Programming Feature Selection. Journal of Machine Learning Research 11, 1491–1516 (2010)MATHGoogle Scholar
  5. 5.
    Wu, O., Zuo, H., Zhu, M., Hu, W., Gao, J., Wang, H.: Rank aggregation based text feature selection. In: Web Intelligence, pp. 165–172 (2009)Google Scholar
  6. 6.
    Bouaguel, W., Bel Mufti, G.: An improvement direction for filter selection techniques using information theory measures and quadratic optimization. International Journal of Advanced Research in Artificial Intelligence 1, 7–11 (2012)CrossRefGoogle Scholar
  7. 7.
    Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proceedings of the Ninth International Workshop on Machine Learning, pp. 249–256. Morgan Kaufmann Publishers Inc., San Francisco (1992)Google Scholar
  8. 8.
    Yu, L., Liu, H.: Feature selection for high-dimensional data: A fast correlation-based filter solution. In: ICML, pp. 856–863 (2003)Google Scholar
  9. 9.
    Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: ICML, pp. 359–366 (2000)Google Scholar
  10. 10.
    Arauzo-Azofra, A., Benitez, J.M., Castro, J.L.: Consistency measures for feature selection. J. Intell. Inf. Syst. 30(3), 273–292 (2008)CrossRefGoogle Scholar
  11. 11.
    Kuncheva, L.I., Bezdek, J.C., Duin, P.W.: Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognition 34, 299–314 (2001)CrossRefMATHGoogle Scholar
  12. 12.
    Wald, R., Khoshgoftaar, T.M., Dittman, D.J.: Mean aggregation versus robust rank aggregation for ensemble gene selection. In: ICMLA (1), pp. 63–69 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Waad Bouaguel
    • 1
  • Ghazi Bel Mufti
    • 2
  • Mohamed Limam
    • 1
    • 3
  1. 1.LARODEC, ISGUniversity of TunisTunisia
  2. 2.LARIME, ESSECUniversity of TunisTunisia
  3. 3.Dhofar UniversityOman

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