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A MIP-based approach to learn MR-Sort models with single-peaked preferences

Author

Listed:
  • Pegdwendé Minoungou

    (Université Paris-Saclay
    IBM)

  • Vincent Mousseau

    (Université Paris-Saclay)

  • Wassila Ouerdane

    (Université Paris-Saclay)

  • Paolo Scotton

    (IBM Research – Zurich)

Abstract

The Majority Rule Sorting (MR-Sort) method assigns alternatives evaluated on multiple criteria to one of the predefined ordered categories. The Inverse MR-Sort problem (Inv-MR-Sort) consists in computing MR-Sort parameters that match a dataset. Existing learning algorithms for Inv-MR-Sort consider monotone preference on criteria. We extend this problem to the case where the preference on criteria are not necessarily monotone, but possibly single-peaked (or single-valley). We propose a mixed-integer programming based algorithm that learns from the training data the preference on criteria together with the other MR-Sort parameters. Numerical experiments investigate the performance of the algorithm, and we illustrate its use on a real-world case study.

Suggested Citation

  • Pegdwendé Minoungou & Vincent Mousseau & Wassila Ouerdane & Paolo Scotton, 2023. "A MIP-based approach to learn MR-Sort models with single-peaked preferences," Annals of Operations Research, Springer, vol. 325(2), pages 795-817, June.
    • Handle: RePEc:spr:annopr:v:325:y:2023:i:2:d:10.1007_s10479-022-05007-5
    DOI: 10.1007/s10479-022-05007-5
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    References listed on IDEAS

    as
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