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Heuristics for feature selection in mathematical programming discriminant analysis models

Author

Listed:
  • K Falangis

    (University of Edinburgh)

  • J J Glen

    (University of Edinburgh)

Abstract

In developing a classification model for assigning observations of unknown class to one of a number of specified classes using the values of a set of features associated with each observation, it is often desirable to base the classifier on a limited number of features. Mathematical programming discriminant analysis methods for developing classification models can be extended for feature selection. Classification accuracy can be used as the feature selection criterion by using a mixed integer programming (MIP) model in which a binary variable is associated with each training sample observation, but the binary variable requirements limit the size of problems to which this approach can be applied. Heuristic feature selection methods for problems with large numbers of observations are developed in this paper. These heuristic procedures, which are based on the MIP model for maximizing classification accuracy, are then applied to three credit scoring data sets.

Suggested Citation

  • K Falangis & J J Glen, 2010. "Heuristics for feature selection in mathematical programming discriminant analysis models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(5), pages 804-812, May.
    • Handle: RePEc:pal:jorsoc:v:61:y:2010:i:5:d:10.1057_jors.2009.24
    DOI: 10.1057/jors.2009.24
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    References listed on IDEAS

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    1. Glen, J.J., 2006. "A comparison of standard and two-stage mathematical programming discriminant analysis methods," European Journal of Operational Research, Elsevier, vol. 171(2), pages 496-515, June.
    2. Antonie Stam, 1997. "Nontraditional approaches to statistical classification: Some perspectives on L_p-norm methods," Annals of Operations Research, Springer, vol. 74(0), pages 1-36, November.
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    4. Selwyn Piramuthu, 1999. "Feature Selection for Financial Credit-Risk Evaluation Decisions," INFORMS Journal on Computing, INFORMS, vol. 11(3), pages 258-266, August.
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    6. Gary J. Koehler, 1991. "Linear Discriminant Functions Determined by Genetic Search," INFORMS Journal on Computing, INFORMS, vol. 3(4), pages 345-357, November.
    7. Stam, Antonie & Joachimsthaler, Erich A., 1990. "A comparison of a robust mixed-integer approach to existing methods for establishing classification rules for the discriminant problem," European Journal of Operational Research, Elsevier, vol. 46(1), pages 113-122, May.
    8. P. S. Bradley & O. L. Mangasarian & W. N. Street, 1998. "Feature Selection via Mathematical Programming," INFORMS Journal on Computing, INFORMS, vol. 10(2), pages 209-217, May.
    9. J J Glen, 1999. "Integer programming methods for normalisation and variable selection in mathematical programming discriminant analysis models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(10), pages 1043-1053, October.
    10. Freed, Ned & Glover, Fred, 1981. "Simple but powerful goal programming models for discriminant problems," European Journal of Operational Research, Elsevier, vol. 7(1), pages 44-60, May.
    11. Stam, Antonie, 1990. "Extensions of mathematical programming-based classification rules: A multicriteria approach," European Journal of Operational Research, Elsevier, vol. 48(3), pages 351-361, October.
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    Cited by:

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    2. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.

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