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Fuzzy goal programming model for classification problems

Author

Listed:
  • Soulef Smaoui

    (Sfax University)

  • Belaid Aouni

    (Qatar University)

Abstract

The aim of this paper is to propose a new approach, based on fuzzy goal programming, for classification problems where the cut-off value c corresponding to the discriminant axe is considered as imprecise. The fuzziness was handled through different membership functions. The proposed model will be illustrated through two and multi-groups classification problems.

Suggested Citation

  • Soulef Smaoui & Belaid Aouni, 2017. "Fuzzy goal programming model for classification problems," Annals of Operations Research, Springer, vol. 251(1), pages 141-160, April.
    • Handle: RePEc:spr:annopr:v:251:y:2017:i:1:d:10.1007_s10479-015-1908-3
    DOI: 10.1007/s10479-015-1908-3
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    References listed on IDEAS

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    1. 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.
    2. Willy Gochet & Antonie Stam & V. Srinivasan & Shaoxiang Chen, 1997. "Multigroup Discriminant Analysis Using Linear Programming," Operations Research, INFORMS, vol. 45(2), pages 213-225, April.
    3. Hojati, Mehran & Bector, C. R. & Smimou, Kamal, 2005. "A simple method for computation of fuzzy linear regression," European Journal of Operational Research, Elsevier, vol. 166(1), pages 172-184, October.
    4. 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.
    5. Sueyoshi, Toshiyuki, 2001. "Extended DEA-Discriminant Analysis," European Journal of Operational Research, Elsevier, vol. 131(2), pages 324-351, June.
    6. Sueyoshi, Toshiyuki, 2006. "DEA-Discriminant Analysis: Methodological comparison among eight discriminant analysis approaches," European Journal of Operational Research, Elsevier, vol. 169(1), pages 247-272, February.
    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. H. Hassanpour & H. R. Maleki & M. A. Yaghoobi, 2009. "A Goal Programming Approach To Fuzzy Linear Regression With Non-Fuzzy Input And Fuzzy Output Data," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 26(05), pages 587-604.
    9. Sueyoshi, Toshiyuki, 1999. "DEA-discriminant analysis in the view of goal programming," European Journal of Operational Research, Elsevier, vol. 115(3), pages 564-582, June.
    10. Unknown, 1986. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 1(4), pages 1-9.
    11. Sueyoshi, Toshiyuki, 2004. "Mixed integer programming approach of extended DEA-discriminant analysis," European Journal of Operational Research, Elsevier, vol. 152(1), pages 45-55, January.
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