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Solving mixed integer classification problems by decomposition

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  • Paul Rubin

Abstract

Research into the accuracy of mixed integer programming models for discrimination and classification, and the efficacy of heuristics developed for them, has been hampered by the inability to solve to optimality problems with moderate to large sample sizes. We present encouraging preliminary results for a decomposition approach that allows solution of models with dimensions previously considered prohibitive. Copyright Kluwer Academic Publishers 1997

Suggested Citation

  • Paul Rubin, 1997. "Solving mixed integer classification problems by decomposition," Annals of Operations Research, Springer, vol. 74(0), pages 51-64, November.
    • Handle: RePEc:spr:annopr:v:74:y:1997:i:0:p:51-64:10.1023/a:1018990909155
    DOI: 10.1023/A:1018990909155
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    Citations

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    Cited by:

    1. Asparoukhov, Ognian K. & Krzanowski, Wojtek J., 2001. "A comparison of discriminant procedures for binary variables," Computational Statistics & Data Analysis, Elsevier, vol. 38(2), pages 139-160, December.
    2. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.
    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. Adem, Jan & Gochet, Willy, 2006. "Mathematical programming based heuristics for improving LP-generated classifiers for the multiclass supervised classification problem," European Journal of Operational Research, Elsevier, vol. 168(1), pages 181-199, January.
    5. Hanif Sherali & Justin Hill, 2013. "Configuration of airspace sectors for balancing air traffic controller workload," Annals of Operations Research, Springer, vol. 203(1), pages 3-31, March.
    6. Gianni Codato & Matteo Fischetti, 2006. "Combinatorial Benders' Cuts for Mixed-Integer Linear Programming," Operations Research, INFORMS, vol. 54(4), pages 756-766, August.
    7. Brandner, Hubertus & Lessmann, Stefan & Voß, Stefan, 2013. "A memetic approach to construct transductive discrete support vector machines," European Journal of Operational Research, Elsevier, vol. 230(3), pages 581-595.

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