Preference learning and multiple criteria decision aiding: differences, commonalities, and synergies. II. (English) Zbl 07922434
Summary: This article elaborates on the connection between multiple criteria decision aiding (MCDA) and preference learning (PL), two research fields with different roots and developed in different communities. It complements the first part of the paper, in which we started with a review of MCDA. In this part, a similar review will be given for PL, followed by a systematic comparison of both methodologies, as well as an overview of existing work on combining PL and MCDA. Our main goal is to stimulate further research at the junction of these two methodologies.
For Part I see [ibid. 22, No. 2, 179–209 (2024; Zbl 07873849)].
For Part I see [ibid. 22, No. 2, 179–209 (2024; Zbl 07873849)].
MSC:
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 90B50 | Management decision making, including multiple objectives |
| 91B06 | Decision theory |
| 91B08 | Individual preferences |
Keywords:
preference learning; preference modelling; multiple criteria decision aiding; multiple criteria decision making; machine learningCitations:
Zbl 07873849Software:
CP-netsReferences:
| [1] | Abbaszadeh, S.; Hüllermeier, E., Machine learning with the Sugeno integral: the case of binary classification, IEEE Trans Fuzzy Syst, 29, 12, 3723-3733, 2021 · doi:10.1109/TFUZZ.2020.3026144 |
| [2] | Aggarwal, M.; Tehrani, A.; Hüllermeier, E., Preference-based learning of ideal solutions in TOPSIS-like decision models, J Multi-Criter Decis Anal, 22, 3-4, 2014 |
| [3] | Aissi, H.; Roy, B.; Ehrgott, M.; Figueira, J.; Greco, S., Robustness in multi-criteria decision aiding, Trends in multiple criteria decision analysis, 87-121, 2010, Springer · Zbl 1200.90095 · doi:10.1007/978-1-4419-5904-1_4 |
| [4] | Akrour R, Schoenauer M, Sebag M (2012) APRIL: active preference learning-based reinforcement learning. In: Proceedings ECML/PKDD, European conference on machine learning and knowledge discovery in databases, Part II. Springer, Bristol, UK, pp 116-131 |
| [5] | Anderson D, Keller J, Havens T (2010) Learning fuzzy-valued fuzzy measures for the fuzzy-valued Sugeno fuzzy integral. In: Proceedings IPMU, international conference on information processing and management of uncertainty in knowledge-based systems. Springer |
| [6] | Askell A, Bai Y, Chen A, et al (2021) A general language assistant as a laboratory for alignment. arXiv:2112.00861 |
| [7] | Atan O, Zame W, van der Schaar M (2018) Learning optimal policies from observational data. In: Proceedings ICML 35th international confference on machine learning, Stockholm, Sweden |
| [8] | Auer, P.; Cesa-Bianchi, N.; Fischer, P., Finite-time analysis of the multiarmed bandit problem, Mach Learn, 47, 235-256, 2002 · Zbl 1012.68093 · doi:10.1023/A:1013689704352 |
| [9] | Bachman P, Sordoni A, Trischler A (2017) Learning algorithms for active learning. In: Proceedings ICML, 34th international conference on machine learning, pp 301-310 |
| [10] | Beliakov G (2008) Fitting fuzzy measures by linear programming. Programming library fmtools. In: Proceedings FUZZ-IEEE 2008, IEEE international conference on fuzzy systems, Piscataway, N.J., pp 862-867 |
| [11] | Beliakov, G.; Divakov, D., On representation of fuzzy measures for learning Choquet and Sugeno integrals, Knowl Bas Syst, 189, 1-5, 2020 |
| [12] | Beliakov, G.; James, S., Citation-based journal ranks: the use of fuzzy measures, Fuzzy Sets Syst, 167, 1, 101-119, 2010 · Zbl 1402.62030 · doi:10.1016/j.fss.2010.08.011 |
| [13] | Beliakov, G.; James, S.; Li, G., Learning Choquet integral-based metrics in semi-supervised classification, IEEE Trans Fuzzy Syst, 19, 562-574, 2011 · doi:10.1109/TFUZZ.2011.2123899 |
| [14] | Beliakov, G.; Gagolewski, M.; James, S., DC optimization for constructing discrete Sugeno integrals and learning nonadditive measures, Optimization, 69, 12, 2515-2534, 2020 · Zbl 1473.90125 · doi:10.1080/02331934.2019.1705300 |
| [15] | Bengs, V.; Busa-Fekete, R.; Mesaoudi-Paul, AE, Preference-based online learning with dueling bandits: A survey, J Mach Learn Res, 22, 7, 1-108, 2021 · Zbl 1539.68248 |
| [16] | Beygelzimer A, Langford J (2009) The offset tree for learning with partial labels. In: Proceedings KDD, 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 129-138 |
| [17] | Bhowmik A, Chen M, Xing Z et al (2019) Estimagg: a learning framework for groupwise aggregated data. In: Proceedings SIAM international conference on data mining. SIAM, Calgary, Alberta, Canada, pp 477-485 |
| [18] | Bhowmik A, Ghosh J, Koyejo O (2016) Sparse parameter recovery from aggregated data. In: Proceedings ICML, 33nd international conference on machine learning, JMLR workshop and conference. Proceedings, vol 48. JMLR.org, New York City, NY, USA, pp 1090-1099 |
| [19] | Bottou, L.; Peters, J.; Candela, J., Counterfactual reasoning and learning systems: the example of computational advertising, J Mach Learn Res, 14, 1, 3207-3260, 2013 · Zbl 1318.62206 |
| [20] | Boutilier, C.; Brafman, R.; Domshlak, C., CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements, J AI Res, 21, 135-191, 2004 · Zbl 1080.68685 |
| [21] | Branke, J.; Corrente, S.; Greco, S., Using Choquet integral as preference model in interactive evolutionary multiobjective optimization, Eur J Oper Res, 250, 884-901, 2016 · Zbl 1346.90731 · doi:10.1016/j.ejor.2015.10.027 |
| [22] | Brazdil, P.; van Rijn, J.; Soares, C., Metalearning: applications to automated machine learning and data mining, 2022, Heidelberg, Berlin: Springer, Heidelberg, Berlin · doi:10.1007/978-3-030-67024-5 |
| [23] | Bresson R, Cohen J, Hüllermeier E, et al (2020) Neural representation and learning of hierarchical Choquet integrals. In: Proceedings IJCAI 2020, 29th international joint conference on artificial intelligence, Yokohama, Japan, pp 1984-1991 |
| [24] | Brost B, Seldin Y, Cox IJ, et al (2016) Multi-dueling bandits and their application to online ranker evaluation. In: Proceedings of ACM international conference on information and knowledge management (CIKM), pp 2161-2166 |
| [25] | Busa-Fekete, R.; Szörényi, B.; Weng, P., Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm, Mach Learn, 97, 3, 327-351, 2014 · Zbl 1319.68170 · doi:10.1007/s10994-014-5458-8 |
| [26] | Busse L, Orbanz P, Buhmann J (2007) Cluster analysis of heterogeneous rank data. In: Proceedings ICML, 24th international conference on machine learning, Corvallis, OR |
| [27] | Cao Z, Qin T, Liu T, et al (2007) Learning to rank: from pairwise approach to listwise approach. In: Proceedings ICML, 24th international conference on machine learning, pp 129-136 |
| [28] | Casteluccia C, Métayer DL (2019) Understanding algorithmic decision making: opportunities and challenges. Tech. Rep. PE 624.261, EPRS Study, European Parlament |
| [29] | Cendrowska, J., PRISM: an algorithm for inducing modular rules, Int J Man-Mach Stud, 27, 349-370, 1987 · Zbl 0638.68110 · doi:10.1016/S0020-7373(87)80003-2 |
| [30] | Chen X, Bennett P, Collins-Thompson K, et al (2013) Pairwise ranking aggregation in a crowdsourced setting. In: Proceedings WSDM, Sixth ACM international conference on web search and data mining, Rome, Italy, pp 193-202 |
| [31] | Cheng W, Dembczynski K, Hüllermeier E (2010) Label ranking based on the Plackett-Luce model. In: Proceedings ICML, international conference on machine learning, Haifa, Israel, pp 215-222 |
| [32] | Christiano P, Leike J, Brown T, et al (2017) Deep reinforcement learning from human preferences. In: Advances in Neural Information Processing Systems, vol 30. Curran Associates, Inc., Red Hook, NY, https://proceedings.neurips.cc/paper/2017/hash/d5e2c0adad503c91f91df240d0cd4e49-Abstract.html |
| [33] | Cinelli, M.; Kadziński, M.; Miebs, G.; Gonzalez, M.; Słowiński, R., Recommending multiple criteria decision analysis methods with a new taxonomy-based decision support system, Eur J Opera Res, 302, 2, 633-651, 2022 · Zbl 1507.90078 · doi:10.1016/j.ejor.2022.01.011 |
| [34] | Cohen, WW; Schapire, RE; Singer, Y.; Jordan, MI; Kearns, MJ; Solla, SA, Learning to order things, Advances in neural information processing systems, 1998, Massachusetts: The MIT Press, Cambridge, Massachusetts |
| [35] | Corrente, S.; Greco, S.; Kadziński, M., Robust ordinal regression in preference learning and ranking, Mach Learn, 93, 2, 381-422, 2013 · Zbl 1300.68040 · doi:10.1007/s10994-013-5365-4 |
| [36] | Corrente, S.; Greco, S.; Matarazzo, B., Explainable interactive evolutionary multiobjective optimization, OMEGA, 122, 2024 · doi:10.1016/j.omega.2023.102925 |
| [37] | Cortez, P.; Cerdeira, A.; Almeida, F., Modeling wine preferences by data mining from physicochemical properties, Decis Supp Syst, 47, 4, 547-553, 2009 · doi:10.1016/j.dss.2009.05.016 |
| [38] | Cour, T.; Sapp, B.; Taskar, B., Learning from partial labels, J Mach Learn Res, 12, 1501-1536, 2011 · Zbl 1280.68162 |
| [39] | Deb K, Thiele L, Laumanns M, et al (2002) Scalable multi-objective optimization test problems. In: Proceedings of the congress on evolutionary computation (CEC-2002), pp 825-830 |
| [40] | Dembczyński, K.; Kotłowski, W.; Słowiński, R., Learning rule ensembles for ordinal classification with monotonicity constraints, Fund Inf, 94, 2, 163-178, 2009 · Zbl 1192.68512 |
| [41] | Dembczyński, K.; Kotłowski, W.; Słowiński, R.; Fürnkranz, J.; Hüllermeier, E., Learning of rule ensembles for multiple attribute ranking problems, Prefere learning, 217-247, 2010, Berlin: Springer, Berlin · Zbl 1214.68279 · doi:10.1007/978-3-642-14125-6_11 |
| [42] | Denoeux, T., A k-nearest neighbor classification rule based on Dempster-Shafer Theory, IEEE Trans Syst Man Cybern, 25, 5, 804-813, 1995 · doi:10.1109/21.376493 |
| [43] | Denoeux, T.; Zouhal, L., Handling possibilistic labels in pattern classification using evidential reasoning, Fuzzy Sets Syst, 122, 47-62, 2001 · Zbl 1063.68635 · doi:10.1016/S0165-0114(00)00086-5 |
| [44] | Dietterich, T.; Lathrop, R.; Lozano-Perez, T., Solving the multiple-instance problem with axis-parallel rectangles, Artif Intel, 89, 31-71, 1997 · Zbl 1042.68650 · doi:10.1016/S0004-3702(96)00034-3 |
| [45] | Doumpos, M.; Marinakis, Y.; Marinaki, M., An evolutionary approach to construction of outranking models for multicriteria classification: the case of the ELECTRE TRI method, Eur J Oper Res, 199, 2, 496-505, 2009 · Zbl 1176.90283 · doi:10.1016/j.ejor.2008.11.035 |
| [46] | Du, X.; Zare, A.; Anderson, D., Multiple instance Choquet integral with binary fuzzy measures for remote sensing classifier fusion with imprecise labels, SSCI 2019, 1154-1162, 2019, Xiamen, China: IEEE Symposium Series on Computational Intelligence, Xiamen, China |
| [47] | Dwork C, Kumar S, Naor M et al (2001) Rank aggregation methods for the Web. In: Proceedings World Wide Web, Hong Kong, pp 613-622 |
| [48] | Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P., Advances in knowledge discovery and data mining, 1996, Menlo Park and Cambridge, MA, USA: AAAI Press and MIT Press, Menlo Park and Cambridge, MA, USA |
| [49] | Fernandez C, Provost F (2019) Observational vs experimental data when making automated decisions using machine learning. Tech. rep., NYU Stern School of Business, SSRN:https://ssrn.com/abstract=3444678 |
| [50] | Fishburn, P., Utility-Theory for Decision Making, 1969, Hoboken, New Jersey, U.S.: Wiley, Hoboken, New Jersey, U.S. |
| [51] | Foulds, J.; Frank, E., A review of multi-instance learning assumptions, Knowl Eng Rev, 25, 1-25, 2010 · doi:10.1017/S026988890999035X |
| [52] | Fürnkranz, J.; Hüllermeier, E.; Cheng, W., Preference-based reinforcement learning: A formal framework and a policy iteration algorithm, Mach Learn, 89, 1, 123-156, 2012 · Zbl 1260.68328 · doi:10.1007/s10994-012-5313-8 |
| [53] | Fürnkranz J, Hüllermeier E, Vanderlooy S (2009) Binary decomposition methods for multipartite ranking. In: Proceedings ECML/PKDD 2009, European Conference on Machine Learning and Knowledge Discovery in Databases, Bled, Slovenia |
| [54] | Gagolewski, M.; James, S.; Beliakov, G., Supervised learning to aggregate data with the Sugeno integral, IEEE Trans Fuzzy Syst, 27, 4, 810-815, 2019 · doi:10.1109/TFUZZ.2019.2895565 |
| [55] | Gill R, Laan M, Robins J (1997) Coarsening at random: Characterizations, conjectures, counter-examples. In: Proceedings of the 1st Seattle symposium in biostatistics: Survival Analysis. Springer US, New York, p 255-294 · Zbl 0918.62003 |
| [56] | Gonzalez J, Dai Z, Damianou A, et al (2017) Preferential Bayesian optimization. arXiv:1704.03651 |
| [57] | Grabisch M (1995) A new algorithm for identifying fuzzy measures and its application to pattern recognition. In: Proceedings IEEE international conference on fuzzy systems, pp 145-150 |
| [58] | Grabisch M (2003) Modelling data by the Choquet integral. In: Information fusion in data mining. Springer, pp 135-148 · Zbl 1052.28012 |
| [59] | Grabisch, M.; Labreuche, C.; Figueira, J.; Greco, S.; Ehrgott, M., Fuzzy measures and integrals in MCDA, Multiple criteria decision analysis: state of the art surveys, 563-604, 2005, New York: Springer, New York · Zbl 1072.90533 · doi:10.1007/0-387-23081-5_14 |
| [60] | Grabisch, M.; Nicolas, JM, Classification by fuzzy integral: performance and tests, Fuzzy Sets Syst, 65, 2-3, 255-271, 1994 · doi:10.1016/0165-0114(94)90023-X |
| [61] | Grabisch, M.; Kojadinovic, I.; Meyer, P., A review of methods for capacity identification in Choquet integral based multi-attribute utility theory, Eur J Oper Res, 186, 766-785, 2008 · Zbl 1138.90407 · doi:10.1016/j.ejor.2007.02.025 |
| [62] | Grandvalet, Y., Logistic regression for partial labels, IPMU, 1935-1941, 2002, Annecy, France: Int. Conf. Information Processing and Management of Uncertainty in Knowledge-Based Systems, Annecy, France |
| [63] | Guo, P., One-shot decision theory, IEEE Trans Syst Man Cybern Part A Syst Humans, 41, 5, 917-926, 2011 · doi:10.1109/TSMCA.2010.2093891 |
| [64] | Haddenhorst B, Bengs V, Hüllermeier E (2021) Identification of the generalized Condorcet winner in multi-dueling bandits. In: Proceedings NeurIPS, annual conference on neural information processing systems 2021, pp 25904-25916 |
| [65] | Har-Peled S, Roth D, Zimak D (2002) Constraint classification: a new approach to multiclass classification. In: Proceedings 13th international conference on algorithmic learning theory. Springer, Lübeck, Germany, pp 365-379 · Zbl 1024.68081 |
| [66] | Heitjan, D.; Rubin, D., Ignorability and coarse data, Ann Stat, 19, 4, 2244-2253, 1991 · Zbl 0745.62004 · doi:10.1214/aos/1176348396 |
| [67] | Henzgen, S.; Hüllermeier, E., Mining rank data, IEEE/ACM Trans Knowl Discov Data, 13, 6, 2019 |
| [68] | Hüllermeier E (2021) Prescriptive machine learning for automated decision making: Challenges and opportunities. CoRR. arXiv:2112.08268 |
| [69] | Hüllermeier, E., Learning from imprecise and fuzzy observations: data disambiguation through generalized loss minimization, Int J Approx Reason, 55, 7, 1519-1534, 2014 · Zbl 1407.68396 · doi:10.1016/j.ijar.2013.09.003 |
| [70] | Hüllermeier, E.; Fürnkranz, J., On predictive accuracy and risk minimization in pairwise label ranking, J Comput Syst Sci, 76, 1, 49-62, 2010 · Zbl 1186.68369 · doi:10.1016/j.jcss.2009.05.005 |
| [71] | Hüllermeier, E.; Vanderlooy, S., Why fuzzy decision trees are good rankers, IEEE Trans Fuzzy Syst, 17, 6, 1233-1244, 2009 · doi:10.1109/TFUZZ.2009.2026640 |
| [72] | Hüllermeier, E.; Fürnkranz, J.; Cheng, W., Label ranking by learning pairwise preferences, Artif Intel, 172, 1897-1917, 2008 · Zbl 1184.68403 · doi:10.1016/j.artint.2008.08.002 |
| [73] | Hussein, A.; Gaber, M.; Elyan, E., Imitation learning: a survey of learning methods, ACM Comput Surv, 50, 2, 1-35, 2017 · doi:10.1145/3054912 |
| [74] | Jin R, Ghahramani Z (2002) Learning with multiple labels. In: 16th annual conference on neural information processing systems, Vancouver, Canada |
| [75] | Joachims T (2002) Optimizing search engines using clickthrough data. In: KDD-2002, 8th ACM SIGKDD international conference on knowledge discovery and data mining, Edmonton, Alberta, Canada |
| [76] | Kadziński, M.; Szczepański, A., Learning the parameters of an outranking-based sorting model with characteristic class profiles from large sets of assignment examples, Appl Soft Comput, 116, 2022 · doi:10.1016/j.asoc.2021.108312 |
| [77] | Kadziński, M.; Słowiński, R.; Greco, S., Multiple criteria ranking and choice with all compatible minimal cover sets of decision rules, Knowl Based Syst, 89, 569-583, 2004 · doi:10.1016/j.knosys.2015.09.004 |
| [78] | Kallus N (2017) Recursive partitioning for personalization using observational data. In: Proceedings ICML, 34th international conference on machine learning, pp 1789-1798 |
| [79] | Kleinberg, J.; Lakkaraju, H.; Leskovec, J., Human decisions and machine predictions, Quart J Econ, 133, 1, 237-293, 2018 · Zbl 1405.91119 |
| [80] | Lepri, B.; Oliver, N.; Letouzé, E., Fair, transparent, and accountable algorithmic decision-making processes, Philos Technol, 31, 611-627, 2018 · doi:10.1007/s13347-017-0279-x |
| [81] | Leroy A, Mousseau V, Pirlot M (2011) Learning the parameters of a multiple criteria sorting method. In: Brafman R, Roberts F, Tsoukias A (eds) Algorithmic decision theory – second international conference, ADT 2011, Piscataway, NJ, USA, October 26-28, 2011. Proceedings, Springer, pp 219-233 · Zbl 1233.90247 |
| [82] | Liu, T., Learning to Rank for Information Retrieval, 2011, Heidelberg, Berlin: Springer, Heidelberg, Berlin · Zbl 1227.68002 · doi:10.1007/978-3-642-14267-3 |
| [83] | Liu, J.; Liao, X.; Kadziński, M., Preference disaggregation within the regularization framework for sorting problems with multiple potentially non-monotonic criteria, Eur J Oper Res, 276, 3, 1071-1089, 2019 · Zbl 1430.90335 · doi:10.1016/j.ejor.2019.01.058 |
| [84] | Liu, J.; Kadziński, M.; Liao, X., A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples, Eur J Oper Res, 286, 3, 963-985, 2020 · Zbl 1443.90220 · doi:10.1016/j.ejor.2020.04.013 |
| [85] | Liu, J.; Kadziński, M.; Liao, X., Data-driven preference learning methods for value-driven multiple criteria sorting with interacting criteria, INFORMS J Comput, 33, 2, 586-606, 2021 · Zbl 1466.90041 |
| [86] | Liu L, Dietterich T (2012) A conditional multinomial mixture model for superset label learning. In: Proceedings NIPS, 26th annual conference on neural information processing systems |
| [87] | Lowd D, Meek C (2005) Adversarial learning. In: Proceedings KDD, 11th ACM SIGKDD international conference on knowledge discovery and data mining, pp 641-647, doi:10.1145/1081870.1081950 |
| [88] | Martyn, K.; Kadziński, M., Deep preference learning for multiple criteria decision analysis, Eur J Oper Resarch, 305, 2, 781-805, 2023 · Zbl 1541.90208 · doi:10.1016/j.ejor.2022.06.053 |
| [89] | Melnikov, V.; Hüllermeier, E., Learning to aggregate: Tackling the aggregation/disaggregation problem for OWA, Proc, 1110-1125, 2019, Asian Conf. on Machine Learning: ACML, Asian Conf. on Machine Learning |
| [90] | Mercer, J., Functions of positive and negative type, and their connection with the theory of integral equations, Philos Trans R Soc London Ser A, 209, 415-446, 1909 · JFM 40.0408.02 · doi:10.1098/rsta.1909.0016 |
| [91] | Mohr F, Bengs V, Hüllermeier E (2021) Single player monte-carlo tree search based on the Plackett-Luce model. In: Proceedings AAAI, Thirty-Fourth AAAI Conference on Artificial Intelligence |
| [92] | Mori T, Murofushi T (1989) An analysis of evaluation model using fuzzy measure and the Choquet integral. In: Proceedings 5th fuzzy system symposium. Japan Society for Fuzzy Sets and Systems, pp 207-212 |
| [93] | Murray, B.; Islam, M.; Pinar, A., Transfer learning for the Choquet integral, FUZZ-IEEE 2019, 1-6, 2019, New Orleans, LA, USA: IEEE Int. Conf. on Fuzzy Systems, New Orleans, LA, USA |
| [94] | Musicant D, Christensen J, Olson J (2007) Supervised learning by training on aggregate outputs. In: Proc. ICDM, 7th IEEE Int. Conf. on Data Mining. IEEE Computer Society, Omaha, Nebraska, USA, pp 252-261, doi:10.1109/ICDM.2007.50 |
| [95] | Nguyen N, Caruana R (2008) Classification with partial labels. In: Proceedings KDD, 14th international conference on knowledge discovery and data mining, Las Vegas, USA |
| [96] | Olteanu AL, Meyer P (2014) Inferring the parameters of a majority rule sorting model with vetoes on large datasets. In: Mousseau V, Pirlot M (eds) DA2PL 2014: From Multicriteria Decision Aid to Preference Learning, pp 87-94 |
| [97] | OpenAI (2022) ChatGPT: Optimizing language models for dialogue. https://openai.com/blog/chatgpt, (accessed 2023-02-02) |
| [98] | Ouyang L, Wu J, Jiang X, et al (2022) Training language models to follow instructions with human feedback. In: Advances in neural information processing systems, pp 27730-27744, https://proceedings.neurips.cc/paper_files/paper/2022/hash/b1efde53be364a73914f58805a001731-Abstract-Conference.html |
| [99] | Pessach D, Singer G, Avrahamia D, et al (2020) Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems 134 |
| [100] | Prade H, Rico A, Serrurier M (2009) Elicitation of Sugeno integrals: A version space learning perspective. In: Proceedings ISMIS, international symposium on methodologies for intelligent systems, LNCS, vol 5722. Springer, pp 392-401 |
| [101] | Rigutini, L.; Papini, T.; Maggini, M., SortNet: learning to rank by a neural preference function, IEEE Trans Neural Netw, 22, 9, 1368-1380, 2011 · doi:10.1109/TNN.2011.2160875 |
| [102] | Roy, B., Multicr Methodol Decision Aid, 1996, Dordrecht: Kluwer Academic Publishers, Dordrecht · Zbl 0893.90108 · doi:10.1007/978-1-4757-2500-1 |
| [103] | Roy, B., Decision science or decision-aid science, Eur J Oper Res, 66, 184-203, 2000 · doi:10.1016/0377-2217(93)90312-B |
| [104] | Roy, B., Robustness in operational research and decision aiding: a multi-faceted issue, Eur J Oper Res, 200, 3, 629-638, 2010 · Zbl 1177.90004 · doi:10.1016/j.ejor.2008.12.036 |
| [105] | Roy, B.; Słowiński, R., Questions guiding the choice of a multicriteria decision aiding method, EURO J Decis Process, 1, 1, 1-29, 2013 |
| [106] | Saha A, Gopalan A (2018) Battle of bandits. In: Proceedings of conference on uncertainty in artificial intelligence (UAI) |
| [107] | Schäfer, D.; Hüllermeier, E., Dyad ranking using Plackett-Luce models based on joint feature representations, Mach Learn, 107, 5, 903-941, 2018 · Zbl 1464.62232 · doi:10.1007/s10994-017-5694-9 |
| [108] | Schulman J, Levine S, Abbeel P, et al (2015) Trust Region Policy Optimization. In: Proceedings of the 32nd international conference on machine learning. PMLR, pp 1889-1897, https://proceedings.mlr.press/v37/schulman15.html |
| [109] | Shalev-Shwartz, S., Online learning and online convex optimization, Found Trends Mach Learn, 4, 2, 107-194, 2011 · Zbl 1253.68190 · doi:10.1561/2200000018 |
| [110] | Slowik A, Bottou L (2021) Algorithmic bias and data bias: Understanding the relation between distributionally robust optimization and data curation. CoRR. http://arxiv.org/abs/2106.09467v1 |
| [111] | Sobrie, O.; Mousseau, V.; Pirlot, M., Learning monotone preferences using a majority rule sorting model, Int Trans Oper Res, 26, 5, 1786-1809, 2019 · Zbl 07766372 · doi:10.1111/itor.12512 |
| [112] | Sobrie O, Mousseau V, Pirlot M (2013) Learning a majority rule model from large sets of assignment examples. In: Proceedings ADT, 3rd international conference on algorithmic decision theory, Bruxelles, Belgium, pp 336-350 · Zbl 1404.68119 |
| [113] | Sobrie O, Mousseau V, Pirlot M (2015) Learning the parameters of a non compensatory sorting model. In: Proceedings ADT, 4th international conference on algorithmic decision theory, Lexington, KY, USA, pp 153-170 · Zbl 1405.90067 |
| [114] | Sutton, R.; Barto, A., Reinforcement learning: an introduction, 2018, Bradford, PA: Bradford Books, Bradford, PA · Zbl 1407.68009 |
| [115] | Swaminathan, A.; Joachims, T., Counterfactual risk minimization: learning from logged bandit feedback, Proc, 814-823, 2015, Int. Conf. on Machine Learning: ICML, Int. Conf. on Machine Learning |
| [116] | Swaminathan A, Joachims T (2015b) The self-normalized estimator for counterfactual learning. In: Proceedings NIPS, advances in neural information processing systems |
| [117] | Tehrani AF, Hüllermeier E (2013) Ordinal Choquistic regression. In: Montero J, Pasi G, Ciucci D (eds) Proceedings EUSFLAT 2013, 8th international conference of the European society for fuzzy logic and technology. Atlantis Press, Milano, Italy, pp 842-849 |
| [118] | Tehrani, AF; Cheng, W.; Dembczynski, K., Learning monotone nonlinear models using the Choquet integral, Mach Learn, 89, 1, 183-211, 2012 · Zbl 1260.68348 |
| [119] | Tehrani, AF; Cheng, W.; Hüllermeier, E., Preference learning using the Choquet integral: the case of multipartite ranking, IEEE Trans Fuzzy Syst, 20, 6, 1102-1113, 2012 · doi:10.1109/TFUZZ.2012.2196050 |
| [120] | Tesauro, G.; Touretzky, D., Connectionist learning of expert preferences by comparison training, Advances in neural information processing systems I, 99-106, 1989, San Francisco, CA: Morgan Kaufmann, San Francisco, CA |
| [121] | Torra, V., OWA operators in data modeling and reidentification, IEEE Trans Fuzzy Syst, 12, 5, 652-660, 2004 · doi:10.1109/TFUZZ.2004.834814 |
| [122] | Torra, V.; Narukawa, Y., Modeling Decisions: Information Fusion and Aggregation Operators, 2007, Berlin: Springer-Verlag, Berlin · doi:10.1007/978-3-540-68791-7 |
| [123] | Tsochantaridis I, Hofmann T, Joachims T, et al (2004) Support vector machine learning for interdependent and structured output spaces. In: Proceedings ICML, Banff, Alberta, Canada |
| [124] | Tukey, J., Exploratory Data Analysis, 1977, Reading, MA, USA: Addison-Wesley, Reading, MA, USA · Zbl 0409.62003 |
| [125] | Tversky, A., Intransitivity of preferences, Psychol Rev, 76, 1, 31-48, 1969 · doi:10.1037/h0026750 |
| [126] | van Engelen, J.; Hoos, H., A survey on semi-supervised learning, Mach Learn, 109, 373-440, 2020 · Zbl 1441.68215 · doi:10.1007/s10994-019-05855-6 |
| [127] | Waegeman W, Baets BD, Boullart L (2009) Kernel-based learning methods for preference aggregation. 4OR 7:169-189 · Zbl 1175.91058 |
| [128] | Ware, M.; Frank, E.; Holmes, G., Interactive machine learning: letting users build classifiers, Int J Human-Comput Stud, 55, 3, 281-292, 2001 · Zbl 0972.68599 · doi:10.1006/ijhc.2001.0499 |
| [129] | Wirth, C.; Akrour, R.; Neumann, G., A survey of preference-based reinforcement learning methods, J Mach Learn Res, 18, 136:1-136:46, 2017 · Zbl 1435.68287 |
| [130] | Yang, Q.; Zhang, Y.; Dai, W., Transfer Learning, 2020, Cambridge, UK: Cambridge University Press, Cambridge, UK · doi:10.1017/9781139061773 |
| [131] | Yue Y, Joachims T (2009) Interactively optimizing information retrieval systems as a dueling bandits problem. In: Proceedings of international conference on machine learning (ICML), pp 1201-1208 |
| [132] | Zagorecki A, Johnson D, Ristvej J (2013) Data mining and machine learning in the context of disaster and crisis management. Int J Emerg Manag 9(4) |
| [133] | Zehlike M, Yang K, Stoyanovich J (2021) Fairness in ranking: A survey. CoRR. arXiv:2103.14000v2 |
| [134] | Zhao, Y.; Zeng, D.; Rush, A., Estimating individualized treatment rules using outcome weighted learning, J Am Stat Assoc, 107, 499, 1106-1118, 2012 · Zbl 1443.62396 · doi:10.1080/01621459.2012.695674 |
| [135] | Zhou, Z., A brief introduction to weakly supervised learning, Natl Sci Rev, 5, 44-53, 2018 · doi:10.1093/nsr/nwx106 |
| [136] | Zitzler E, Laumanns M (2018) MOKP test problems. https://sop.tik.ee.ethz.ch/download/supplementary/testProblemSuite/ |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
