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Constrained discriminant analysis via 0/1 mixed integer programming

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  • Richard Gallagher
  • Eva Lee
  • David Patterson

Abstract

A nonlinear 0/1 mixed integer programming model is presented for a constrained discriminant analysis problem. The model places restrictions on the numbers of misclassifications allowed among the training entities, and incorporates a "reserved judgment" region to which entities whose classifications are difficult to determine may be allocated. Two linearizations of the model are given one heuristic and one exact. Numerical results from real-world machine-learning datasets are presented. Copyright Kluwer Academic Publishers 1997

Suggested Citation

  • Richard Gallagher & Eva Lee & David Patterson, 1997. "Constrained discriminant analysis via 0/1 mixed integer programming," Annals of Operations Research, Springer, vol. 74(0), pages 65-88, November.
    • Handle: RePEc:spr:annopr:v:74:y:1997:i:0:p:65-88:10.1023/a:1018943025993
    DOI: 10.1023/A:1018943025993
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    Cited by:

    1. J. Paul Brooks & Eva K. Lee, 2014. "Solving a Multigroup Mixed-Integer Programming-Based Constrained Discrimination Model," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 567-585, August.
    2. Eva K. Lee & Richard J. Gallagher & David A. Patterson, 2003. "A Linear Programming Approach to Discriminant Analysis with a Reserved-Judgment Region," INFORMS Journal on Computing, INFORMS, vol. 15(1), pages 23-41, February.
    3. Parag Pendharkar & Marvin Troutt, 2014. "Interactive classification using data envelopment analysis," Annals of Operations Research, Springer, vol. 214(1), pages 125-141, March.
    4. J. Paul Brooks, 2011. "Support Vector Machines with the Ramp Loss and the Hard Margin Loss," Operations Research, INFORMS, vol. 59(2), pages 467-479, April.
    5. J. Brooks & Eva Lee, 2010. "Analysis of the consistency of a mixed integer programming-based multi-category constrained discriminant model," Annals of Operations Research, Springer, vol. 174(1), pages 147-168, February.

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