Abstract
Learning the parameters of a Majority Rule Sorting model (MR-Sort) through linear programming requires to use binary variables. In the context of preference learning where large sets of alternatives and numerous attributes are involved, such an approach is not an option in view of the large computing times implied. Therefore, we propose a new metaheuristic designed to learn the parameters of an MR-Sort model. This algorithm works in two phases that are iterated. The first one consists in solving a linear program determining the weights and the majority threshold, assuming a given set of profiles. The second phase runs a metaheuristic which determines profiles for a fixed set of weights and a majority threshold. The presentation focuses on the metaheuristic and reports the results of numerical tests, providing insights on the algorithm behavior.
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References
Yu, W.: Aide multicritère la décision dans le cadre de la problématique du tri: méthodes et applications. PhD thesis, LAMSADE, Université Paris Dauphine, Paris (1992)
Roy, B., Bouyssou, D.: Aide multicritère la décision: méthodes et cas. Economica Paris (1993)
Bouyssou, D., Marchant, T.: An axiomatic approach to noncompensatory sorting methods in MCDM, I: The case of two categories. European Journal of Operational Research 178(1), 217–245 (2007)
Bouyssou, D., Marchant, T.: An axiomatic approach to noncompensatory sorting methods in MCDM, II: More than two categories. European Journal of Operational Research 178(1), 246–276 (2007)
Mousseau, V., Slowinski, R.: Inferring an ELECTRE TRI model from assignment examples. Journal of Global Optimization 12(1), 157–174 (1998)
Mousseau, V., Figueira, J., Naux, J.P.: Using assignment examples to infer weights for ELECTRE TRI method: Some experimental results. European Journal of Operational Research 130(1), 263–275 (2001)
Ngo The, A., Mousseau, V.: Using assignment examples to infer category limits for the ELECTRE TRI method. Journal of Multi-criteria Decision Analysis 11(1), 29–43 (2002)
Dias, L., Mousseau, V., Figueira, J., Clímaco, J.: An aggregation/disaggregation approach to obtain robust conclusions with ELECTRE TRI. European Journal of Operational Research 138(1), 332–348 (2002)
Dias, L., Mousseau, V.: Inferring Electre’s veto-related parameters from outranking examples. European Journal of Operational Research 170(1), 172–191 (2006)
Doumpos, M., Marinakis, Y., Marinaki, M., Zopounidis, C.: An evolutionary approach to construction of outranking models for multicriteria classification: The case of the ELECTRE TRI method. European Journal of Operational Research 199(2), 496–505 (2009)
Leroy, A., Mousseau, V., Pirlot, M.: Learning the parameters of a multiple criteria sorting method. In: Brafman, R. (ed.) ADT 2011. LNCS, vol. 6992, pp. 219–233. Springer, Heidelberg (2011)
Cailloux, O., Meyer, P., Mousseau, V.: Eliciting ELECTRE TRI category limits for a group of decision makers. European Journal of Operational Research 223(1), 133–140 (2012)
Fürnkranz, J., Hüllermeier, E.: Preference learning: An introduction. In: Fürnkranz, J., Hüllermeier, E. (eds.) Preference Learning, pp. 1–17. Springer (2010)
Bouyssou, D., Pirlot, M.: A characterization of concordance relations. European Journal of Operational Research 167/2, 427–443 (2005)
Bouyssou, D., Pirlot, M.: Further results on concordance relations. European Journal of Operational Research 181, 505–514 (2007)
Pirlot, M.: General local search methods. European Journal of Operational Research 92(3), 493–511 (1996)
Butler, J., Jia, J., Dyer, J.: Simulation techniques for the sensitivity analysis of multi-criteria decision models. European Journal of Operational Research 103(3), 531–546 (1997)
Devaud, J., Groussaud, G., Jacquet-Lagrèze, E.: UTADIS: Une méthode de construction de fonctions d’utilité additives rendant compte de jugements globaux. In: European Working Group on MCDA, Bochum, Germany (1980)
Doumpos, M., Zopounidis, C.: Multicriteria Decision Aid Classification Methods. Kluwer Academic Publishers, Dordrecht (2002)
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Sobrie, O., Mousseau, V., Pirlot, M. (2013). Learning a Majority Rule Model from Large Sets of Assignment Examples. In: Perny, P., Pirlot, M., Tsoukiàs, A. (eds) Algorithmic Decision Theory. ADT 2013. Lecture Notes in Computer Science(), vol 8176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41575-3_26
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DOI: https://doi.org/10.1007/978-3-642-41575-3_26
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