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Mathematical programming models for piecewise-linear discriminant analysis

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  • J J Glen

    (University of Edinburgh, Management School)

Abstract

Mathematical programming (MP) discriminant analysis models are widely used to generate linear discriminant functions that can be adopted as classification models. Nonlinear classification models may have better classification performance than linear classifiers, but although MP methods can be used to generate nonlinear discriminant functions, functions of specified form must be evaluated separately. Piecewise-linear functions can approximate nonlinear functions, and two new MP methods for generating piecewise-linear discriminant functions are developed in this paper. The first method uses maximization of classification accuracy (MCA) as the objective, while the second uses an approach based on minimization of the sum of deviations (MSD). The use of these new MP models is illustrated in an application to a test problem and the results are compared with those from standard MCA and MSD models.

Suggested Citation

  • J J Glen, 2005. "Mathematical programming models for piecewise-linear discriminant analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(3), pages 331-341, March.
  • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:3:d:10.1057_palgrave.jors.2601818
    DOI: 10.1057/palgrave.jors.2601818
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    References listed on IDEAS

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    1. 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.
    2. J J Glen, 2001. "Classification accuracy in discriminant analysis: a mixed integer programming approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(3), pages 328-339, March.
    3. O. L. Mangasarian, 1965. "Linear and Nonlinear Separation of Patterns by Linear Programming," Operations Research, INFORMS, vol. 13(3), pages 444-452, June.
    4. Sueyoshi, Toshiyuki, 2001. "Extended DEA-Discriminant Analysis," European Journal of Operational Research, Elsevier, vol. 131(2), pages 324-351, June.
    5. Silva, Antonio Pedro Duarte & Stam, Antonie, 1994. "Second order mathematical programming formulations for discriminant analysis," European Journal of Operational Research, Elsevier, vol. 72(1), pages 4-22, January.
    6. 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.
    7. 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.
    8. 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.
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    Cited by:

    1. Ai-bing Ji & Ye Ji & Yanhua Qiao, 2018. "DEA-Based Piecewise Linear Discriminant Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 809-820, April.
    2. Soulef Smaoui & Belaid Aouni, 2017. "Fuzzy goal programming model for classification problems," Annals of Operations Research, Springer, vol. 251(1), pages 141-160, April.
    3. J J Glen, 2008. "An additive utility mixed integer programming model for nonlinear discriminant analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(11), pages 1492-1505, November.
    4. 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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