UTA Plus

This method may be used to solve problems of choice or of multi criteria classification on set A of actions. It constructs a utility function from a preorder defined by the user on a subset A’ of the reference actions. The procedure, based on a principle of ordinal regression, consist in solving a small linear program. The program provides piecewise linear functions of marginal utility that are as compatible as possible with the given preorder. The user can modify the marginal utility functions within limits given by a sensitivity analysis of the ordinal regression problem. There is a friendly user interface in which to make these changes. The utility function given by the user is then used to define a preorder on the set A of actions.

References in zbMATH (referenced in 150 articles )

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  1. Dias, Luis C.; Dias, Joana; Ventura, Tiago; Rocha, Humberto; Ferreira, Brígida; Khouri, Leila; Lopes, Maria do Carmo: Learning target-based preferences through additive models: an application in radiotherapy treatment planning (2022)
  2. Tlili, Ali; Belahcène, Khaled; Khaled, Oumaima; Mousseau, Vincent; Ouerdane, Wassila: Learning non-compensatory sorting models using efficient SAT/MaxSAT formulations (2022)
  3. Wachowicz, Tomasz; Roszkowska, Ewa: Can holistic declaration of preferences improve a negotiation offer scoring system? (2022)
  4. Arcidiacono, Sally Giuseppe; Corrente, Salvatore; Greco, Salvatore: Robust stochastic sorting with interacting criteria hierarchically structured (2021)
  5. Corrente, S.; Figueira, J. R.; Greco, S.: Pairwise comparison tables within the deck of cards method in multiple criteria decision aiding (2021)
  6. de Almeida, Adiel Teixeira; Frej, Eduarda Asfora; Roselli, Lucia Reis Peixoto: Combining holistic and decomposition paradigms in preference modeling with the flexibility of FITradeoff (2021)
  7. Dias, Luis C.; Oliveira, Gabriela D.; Sarabando, Paula: Choice-based preference disaggregation concerning vehicle technologies (2021)
  8. Grigoroudis, Evangelos; Noel, Laurent; Galariotis, Emilios; Zopounidis, Constantin: An ordinal regression approach for analyzing consumer preferences in the art market (2021)
  9. Montazery, Mojtaba; Wilson, Nic: Scaling-invariant maximum margin preference learning (2021)
  10. Costa, Ana Sara; Corrente, Salvatore; Greco, Salvatore; Figueira, José Rui; Borbinha, José: A robust hierarchical nominal multicriteria classification method based on similarity and dissimilarity (2020)
  11. Viappiani, Paolo; Boutilier, Craig: On the equivalence of optimal recommendation sets and myopically optimal query sets (2020)
  12. Wu, Siqi; Wu, Meng; Dong, Yucheng; Liang, Haiming; Zhao, Sihai: The 2-rank additive model with axiomatic design in multiple attribute decision making (2020)
  13. Angelopoulos, Dimitrios; Siskos, Yannis; Psarras, John: Disaggregating time series on multiple criteria for robust forecasting: the case of long-term electricity demand in Greece (2019)
  14. Dias, Luis C.; Vetschera, Rudolf: On generating utility functions in stochastic multicriteria acceptability analysis (2019)
  15. Belahcène, K.; Labreuche, C.; Maudet, N.; Mousseau, V.; Ouerdane, W.: An efficient SAT formulation for learning multiple criteria non-compensatory sorting rules from examples (2018)
  16. Costa, Ana Sara; Figueira, José Rui; Borbinha, José: A multiple criteria nominal classification method based on the concepts of similarity and dissimilarity (2018)
  17. Hu, Jian; Bansal, Manish; Mehrotra, Sanjay: Robust decision making using a general utility set (2018)
  18. Ishizaka, Alessio; Siraj, Sajid: Are multi-criteria decision-making tools useful? An experimental comparative study of three methods (2018)
  19. Karakaya, G.; Köksalan, M.; Ahipaşaoğlu, S. D.: Interactive algorithms for a broad underlying family of preference functions (2018)
  20. Özpeynirci, Selin; Özpeynirci, Özgür; Mousseau, Vincent: An interactive algorithm for multiple criteria constrained sorting problem (2018)

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