Abstract
The Simple Ranking Method using Reference Profiles (or SRMP) is a Multi-Criteria Decision Aiding technique based on the outranking paradigm, which allows to rank decision alternatives according to the preferences of a decision maker (DM). Inferring the preference parameters of such a model can lead to a cognitive fatigue of the DM, who is often asked to express several preferential statements about pairs of alternatives during the elicitation process. To limit the DM’s effort, we propose in this work an incremental elicitation process to select informative pairs of alternatives to be presented to the DM sequentially with the aim of refining the SRMP model until a robust recommendation can be made. We study several different heuristics for selecting the pair of alternatives to be submitted to the DM at each step. Following extensive numerical experiments we identify one of the proposed heuristics as performing significantly better than the others and we provide several guidelines for its use in practice.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Belahcène K, Mousseau V, Ouerdane W, Pirlot M, Sobrie O (2018) Ranking with multiple reference points: efficient elicitation and learning procedures. In: Proceedings from multiple criteria decision aid to preference learning (DA2PL). https://hal.archives-ouvertes.fr/hal-01956128
Benabbou N, Perny P, Viappiani P (2017) Incremental elicitation of Choquet capacities for multicriteria choice, ranking and sorting problems. Artif Intell 246:152–180. https://doi.org/10.1016/j.artint.2017.02.001. http://www.sciencedirect.com/science/article/pii/S0004370217300152
Bourdache N, Perny P, Spanjaard O (2019) Incremental elicitation of rank-dependent aggregation Functions based on Bayesian linear regression. In: IJCAI-19-twenty-eighth international joint conference on artificial intelligence. International joint conferences on artificial intelligence organization, Macao, China, pp 2023–2029. https://hal.archives-ouvertes.fr/hal-02202468
Boutilier C, Patrascu R, Poupart P, Schuurmans D (2006) Constraint-based optimization and utility elicitation using the minimax decision criterion. Artif Intell 170(8):686–713. https://doi.org/10.1016/j.artint.2006.02.003
Boutilier C, Patrascu R, Poupart P, Schuurmans D (2006b) Constraint-based optimization and utility elicitation using the minimax decision criterion. Artif Intell 170(8–9):686–713
Bouyssou D, Pirlot M (2009) An axiomatic analysis of concordance discordance relations. Eur J Oper Res 199(2):468–477. https://doi.org/10.1016/j.ejor.2008.11.011. http://www.sciencedirect.com/science/article/pii/S0377221708009946
Bouyssou D, Marchant T (2013) Multiattribute preference models with reference points. Eur J Oper Res 229(2):470–481. https://doi.org/10.1016/j.ejor.2013.02.046. http://www.sciencedirect.com/science/article/pii/S0377221713001951
Bouyssou D, Marchant T, Pirlot M, Tsoukiàs A, Vincke P (2006) Evaluation and decision models with multiple criteria: stepping stones for the analyst, 1st edn. In: International series in operations research and management science, vol 86. Boston. http://www.springer.com/sgw/cda/frontpage/0,11855,4-40521-22-116132747-0,00.html
Butler J, Jia J, Dyer J (1997) Simulation techniques for the sensitivity analysis of multi-criteria decision models. Eur J Oper Res 103:531–546
Chajewska U, Koller D, Parr R (2000) Making rational decisions using adaptive utility elicitation. AAAI Press/The MIT Press. http://www.aaai.org/Library/AAAI/2000/aaai00-056.php
Ciomek K, Kadziński M, Tervonen T (2017) Heuristics for selecting pair-wise elicitation questions in multiple criteria choice problems. Eur J Oper Res 262(2):693–707. https://doi.org/10.1016/j.ejor.2017.04.021. http://www.sciencedirect.com/science/article/pii/S0377221717303545
Dodge Y (2008) Kolmogorov–Smirnov test. Springer, New York, pp 283–287
Fan J, Zhang J, Mei K, Peng J, Gao L (2015) Cost-sensitive learning of hierarchical tree classifiers for large-scale image classification and novel category detection. Pattern Recogn 48(5):1673–1687. https://doi.org/10.1016/j.patcog.2014.10.025
Figueira J, Mousseau V, Roy B (2008) ELECTRE methods. In: J. Figueira, S. Greco, M. Ehrgott (eds.) Multiple criteria decision analysis: state of the art surveys. Springer, Boston, Dordrecht, London, , pp 133–162. http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-165-22-34954528-0,00.html
Figueira J, Greco S, Ehrgott M (eds) (2016) Multiple criteria decision analysis: state of the art surveys, 2nd edn. Kluwer Acadamic publication, New York
Fürnkranz J, Hüllermeier E (2011) Preference learning and ranking by pairwise comparison. Springer, Berlin, pp 65–82
Gholipour A, Hosseini MJ, Beigy H (2013) An adaptive regression tree for non-stationary data streams. In: Proceedings of the 28th annual ACM symposium on applied computing, SAC ’13. ACM, New York, pp 815–817. https://doi.org/10.1145/2480362.2480519
Greco S, Matarazzo B, Slowinski R (2001) Rough sets theory for multicriteria decision analysis. Eur J Oper Res 129(1):1–47. https://doi.org/10.1016/S0377-2217(00)00167-3
Holloway H, White CC III (2003) Question selection for multi-attribute decision-aiding. Eur J Oper Res 148(3):525–533. https://doi.org/10.1016/S0377-2217(02)00436-8. http://www.sciencedirect.com/science/article/pii/S0377221702004368
Keeney R, Raiffa H (1976) Decisions with multiple consequences: preferences and value tradeoffs. Wiley, New York
Kendall MG (1938) A new measure of rank correlation. Biometrika 30(1/2):81–93
Liu J, Ouerdane W, Mousseau V (2014) Metaheuristic approach for preference learning in multicriteria ranking based on reference points (2014). In Proceeding of the 2nd wokshop from multiple criteria Decision aid to Preference Learning (DA2PL)
Mousseau V (1995) Eliciting information concerning the relative importance of criteria. In: Pardalos P, Siskos Y, Zopounidis C (eds) Advances in multicriteria analysis. Kluwer Academic, Dordrecht, pp 17–43
Mousseau V, Figueira J, Dias L, Gomes da Silva C, Climaco J (2003) Resolving inconsistencies among constraints on the parameters of an MCDA model. Eur J Oper Res 147(1):72–93. https://doi.org/10.1016/S0377-2217(02)00233-3
Mousseau V, Pirlot M (2015) Preference elicitation and learning. EURO J Decis Process 3(1–2):1
Olteanu A (2018) Strategies for the incremental inference of majority-rule sorting models. In: Proceeding from multiple criteria decision aid to preference learning (DA2PL). https://hal.archives-ouvertes.fr/hal-01947853
Olteanu AL, Belahcène K, Mousseau V, Ouerdane W, Rolland A, Zheng J (2018) Preference elicitation for a ranking method based on multiple reference profiles. https://hal.archives-ouvertes.fr/hal-01862334. Working paper or preprint
Perny P, Rolland A (2006) Reference-dependent qualitative models for decision making under uncertainty. In: Proceedings of the 2006 conference on ECAI 2006: 17th European conference on artificial intelligence, August 29–September 1, 2006, Riva Del Garda, Italy. IOS Press, NLD, pp 422–426
Rolland A (2013) Reference-based preferences aggregation procedures in multi-criteria decision making. Eur J Oper Res 225(3):479–486. https://doi.org/10.1016/j.ejor.2012.10.013
Roy B (1991) The outranking approach and the foundations of ELECTRE methods. Theory Decis 31(1):49–73. https://doi.org/10.1007/BF00134132
Roy B (1996) Multicriteria methodology for decision aiding. Kluwer Academic, Dordrecht
Salperwyck C, Lemaire V (2013) Incremental decision tree based on order statistics. In: The 2013 international joint conference on neural networks (IJCNN), pp 1–8. https://doi.org/10.1109/IJCNN.2013.6706907
Settles B (2012) Active learning, volume 6 of synthesis lectures on artificial intelligence and machine learning. Morgan & Claypool
Syed SS, Aayush A, Kaushik R (2017) Incremental learning in deep convolutional neural networks using partial network sharing. (2017). arXiv:1712.02719
Wen Y, Lu B (2007) Incremental learning of support vector machines by classifier combining. In: Zhou Z, Li H, Yang Q (eds) Advances in knowledge discovery and data mining. Springer, Berlin, Heidelberg, pp 904–911
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Khannoussi, A., Olteanu, AL., Labreuche, C. et al. Simple ranking method using reference profiles: incremental elicitation of the preference parameters. 4OR-Q J Oper Res 20, 499–530 (2022). https://doi.org/10.1007/s10288-021-00487-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10288-021-00487-w