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Inferring robust decision models in multicriteria classification problems: An experimental analysis

Author

Listed:
  • Michael Doumpos
  • Constantin Zopounidis
  • Emilios C. C Galariotis

    (Audencia Recherche - Audencia Business School)

Abstract

Recent research on robust decision aiding has focused on identifying a range of recommendations from preferential information and the selection of representative models compatible with preferential constraints. This study presents an experimental analysis on the relationship between the results of a single decision model (additive value function) and the ones from the full set of compatible models in classification problems. Different optimization formulations for selecting a representative model are tested on artificially generated data sets with varying characteristics.

Suggested Citation

  • Michael Doumpos & Constantin Zopounidis & Emilios C. C Galariotis, 2014. "Inferring robust decision models in multicriteria classification problems: An experimental analysis," Post-Print hal-00961323, HAL.
    • Handle: RePEc:hal:journl:hal-00961323
    DOI: 10.1016/j.ejor.2013.12.034
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    Citations

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    Cited by:

    1. Kadziński, Miłosz & Wójcik, Michał & Ciomek, Krzysztof, 2022. "Review and experimental comparison of ranking and choice procedures for constructing a univocal recommendation in a preference disaggregation setting," Omega, Elsevier, vol. 113(C).
    2. Zhen Zhang & Zhuolin Li, 2023. "Consensus-based TOPSIS-Sort-B for multi-criteria sorting in the context of group decision-making," Annals of Operations Research, Springer, vol. 325(2), pages 911-938, June.
    3. Eduardo Álvarez-Miranda & John Díaz-Guerrero, 2020. "Multicriteria saliency detection: a (exact) robust network design approach," Annals of Operations Research, Springer, vol. 286(1), pages 649-668, March.
    4. Tsionas, Mike G., 2019. "Multi-objective optimization using statistical models," European Journal of Operational Research, Elsevier, vol. 276(1), pages 364-378.
    5. Michalis Doumpos & Alexis Guyot & Emilios Galariotis & Constantin Zopounidis, 2020. "Assessing the quality of life in French municipalities: a multidimensional approach," Annals of Operations Research, Springer, vol. 293(2), pages 789-808, October.
    6. Kadziński, Miłosz & Ghaderi, Mohammad & Dąbrowski, Maciej, 2020. "Contingent preference disaggregation model for multiple criteria sorting problem," European Journal of Operational Research, Elsevier, vol. 281(2), pages 369-387.
    7. Kadziński, Miłosz & Cinelli, Marco & Ciomek, Krzysztof & Coles, Stuart R. & Nadagouda, Mallikarjuna N. & Varma, Rajender S. & Kirwan, Kerry, 2018. "Co-constructive development of a green chemistry-based model for the assessment of nanoparticles synthesis," European Journal of Operational Research, Elsevier, vol. 264(2), pages 472-490.
    8. Liu, Jiapeng & Liao, Xiuwu & Kadziński, Miłosz & Słowiński, Roman, 2019. "Preference disaggregation within the regularization framework for sorting problems with multiple potentially non-monotonic criteria," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1071-1089.
    9. Sobrie, Olivier & Gillis, Nicolas & Mousseau, Vincent & Pirlot, Marc, 2018. "UTA-poly and UTA-splines: Additive value functions with polynomial marginals," European Journal of Operational Research, Elsevier, vol. 264(2), pages 405-418.
    10. A. Spyridakos & N. Tsotsolas & Y. Siskos & D. Yannakopoulos & I. Vryzidis, 2020. "A visualization approach for robustness analysis in multicriteria disaggregation–aggregation approaches," Operational Research, Springer, vol. 20(3), pages 1841-1861, September.
    11. Ciomek, Krzysztof & Ferretti, Valentina & Kadzinski, Milosz, 2018. "Predictive analytics and disused railways requalification: insights from a Post Factum Analysis perspective," LSE Research Online Documents on Economics 85922, London School of Economics and Political Science, LSE Library.
    12. Liu, Jiapeng & Kadziński, Miłosz & Liao, Xiuwu & Mao, Xiaoxin & Wang, Yao, 2020. "A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples," European Journal of Operational Research, Elsevier, vol. 286(3), pages 963-985.
    13. Salvatore Corrente & Michael Doumpos & Salvatore Greco & Roman Słowiński & Constantin Zopounidis, 2017. "Multiple criteria hierarchy process for sorting problems based on ordinal regression with additive value functions," Annals of Operations Research, Springer, vol. 251(1), pages 117-139, April.
    14. Kadziński, Miłosz & Ciomek, Krzysztof, 2021. "Active learning strategies for interactive elicitation of assignment examples for threshold-based multiple criteria sorting," European Journal of Operational Research, Elsevier, vol. 293(2), pages 658-680.
    15. Khaled Belahcène & Vincent Mousseau & Wassila Ouerdane & Marc Pirlot & Olivier Sobrie, 2023. "Multiple criteria sorting models and methods—Part I: survey of the literature," 4OR, Springer, vol. 21(1), pages 1-46, March.
    16. Andreopoulou, Zacharoula & Koliouska, Christiana & Galariotis, Emilios & Zopounidis, Constantin, 2018. "Renewable energy sources: Using PROMETHEE II for ranking websites to support market opportunities," Technological Forecasting and Social Change, Elsevier, vol. 131(C), pages 31-37.
    17. Kadziński, Miłosz & Ciomek, Krzysztof & Słowiński, Roman, 2015. "Modeling assignment-based pairwise comparisons within integrated framework for value-driven multiple criteria sorting," European Journal of Operational Research, Elsevier, vol. 241(3), pages 830-841.

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