A Bayesian model for multicriteria sorting problems

References

• Adams, J.L, Mehrotra, A. and McGlynn, E.A. (2010) Estimating reliability and misclassification in physician profiling. Technical report, Rand Corporation.
   Google Scholar
• Albert, J.H. and Chib, S. (1993) Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88(422), 669–679.
   Web of Science ®Google Scholar
• Auer, P., Cesa-Bianchi, N. and Fischer, P. (2002) Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2), 235–256.
   Web of Science ®Google Scholar
• Barile, N. and Feelders, A. (2012) Active learning with monotonicity constraints, in Proceedings of the 2012 SIAM International Conference on Data Mining, SIAM, Philadelphia, PA, pp. 756–767.
   Google Scholar
• Bastani, H. and Bayati, M. (2020) Online decision making with high-dimensional covariates. Operations Research, 68(1), 276–294.
   Web of Science ®Google Scholar
• Berardi, V.L. and Zhang, G.P. (1999) The effect of misclassification costs on neural network classifiers. Decision Sciences, 30(3), 659–682.
   Web of Science ®Google Scholar
• Berry, D.A. and Fristedt, B. (1985) Bandit Problems: Sequential Allocation of Experiments. Chapman and Hall, London UK.
   Google Scholar
• Bond, S.D., Carlson, K.A. and Keeney, R.L. (2008) Generating objectives: Can decision makers articulate what they want? Management Science, 54(1), 56–70.
   Web of Science ®Google Scholar
• Brezzi, M. and Lai, T.L. (2002) Optimal learning and experimentation in bandit problems. Journal of Economic Dynamics and Control, 27(1), 87–108.
   Web of Science ®Google Scholar
• Buğdaci, A.G., Köksalan, M., Özpeynirci, S. and Serin, Y. (2013) An interactive probabilistic approach to multi-criteria sorting. IIE Transactions, 45(10), 1048–1058.
   Web of Science ®Google Scholar
• Cardoso, J. and da Costa, J.P. (2007) Learning to classify ordinal data: The data replication method. Journal of Machine Learning Research, 8, 1393–1429.
   Web of Science ®Google Scholar
• Chu, W. and Keerthi, S.S. (2005). New approaches to support vector ordinal regression, in Proceedings of the 22nd International Conference on Machine Learning, pp. 145–152.
   Google Scholar
• Cinelli, M., Kadziński, M., Gonzalez, M. and Słowiński, R. (2020) How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy. Omega, 96, 102261.
   Web of Science ®Google Scholar
• Doumpos, M. and Zopounidis, C. (2004) Developing sorting models using preference disaggregation analysis: An experimental investigation. European Journal of Operational Research 154, (3), 585–598.
   Web of Science ®Google Scholar
• Gittins, J.C. (1989) Multi-armed Bandit Allocation Indices. Wiley, New York.
   Google Scholar
• Gutiérrez, P.A., Perez-Ortiz, M., Sanchez-Monedero, J., Fernandez-Navarro, F. and Hervas-Martinez, C. (2015) Ordinal regression methods: Survey and experimental study. IEEE Transactions on Knowledge and Data Engineering, 28(1), 127–146.
   Web of Science ®Google Scholar
• Houlsby, N., Hernández-Lobato, J.M. and Ghahramani, Z. (2014) Cold-start active learning with robust ordinal matrix factorization, in International Conference on Machine Learning, pp. 766–774.
   Google Scholar
• Houlsby, N., Huszár, F., Ghahramani, Z. and Lengyel, M. (2011) Bayesian active learning for classification and preference learning. arXiv preprint arXiv:1112.5745.
   Google Scholar
• Jacquet-Lagreze, E. and Siskos, J. (1982). Assessing a set of additive utility functions for multicriteria decision-making, the UTA method. European Journal of Operational Research, 10(2), 151–164.
   Web of Science ®Google Scholar
• Judge, G.G., Griffiths, W.E., Hill, R.C. and Lee, T.C. (1980) The Theory and Practice of Econometrics. John Wiley & Sons, New York.
   Google Scholar
• Kapoor, A., Horvitz, E. and Basu, S. (2007) Selective supervision: Guiding supervised learning with decision-theoretic active learning. IJCAI, 7, 877–882.
   Google Scholar
• Köksalan, M. and Özpeynirci, S.B. (2009) An interactive sorting method for additive utility functions. Computers & Operations Research, 36(9), 2565–2572.
   Web of Science ®Google Scholar
• Köksalan, M. and Ulu, C. (2003) An interactive approach for placing alternatives in preference classes. European Journal of Operational Research, 144(2), 429–439.
   Web of Science ®Google Scholar
• Lohmann, C. and Ohliger, T. (2019) The total cost of misclassification in credit scoring: A comparison of generalized linear models and generalized additive models. Journal of Forecasting, 38(5), 375–389.
   Web of Science ®Google Scholar
• McCulloch, R.E., Polson, N.G. and Rossi, P.E. (2000) A Bayesian analysis of the multinomial probit model with fully identified parameters. Journal of Econometrics, 99, 173–193.
   Web of Science ®Google Scholar
• Michalowski, W., Rubin, S., Slowinski, R. and Wilk, S. (2001) Triage of the child with abdominal pain: A clinical algorithm for emergency patient management. Paediatrics & Child Health, 6(1), 23–28.
   PubMedGoogle Scholar
• Moro, S., Cortez, P. and Rita, P. (2014) A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62, 22–31.
   Web of Science ®Google Scholar
• Mousseau, V., Dias, L.C. and Figueira, J. (2006) Dealing with inconsistent judgments in multiple criteria sorting models. 4OR, 4(2), 145–158.
   Google Scholar
• Peng, Y., Wang, G., Kou, G. and Shi, Y. (2011) An empirical study of classification algorithm evaluation for financial risk prediction. Applied Soft Computing, 11(2), 2906–2915.
   Web of Science ®Google Scholar
• Perchet, V. and Rigollet, P. (2013) The multi-armed bandit problem with covariates. The Annals of Statistics, 41(2), 693–721.
   Web of Science ®Google Scholar
• Roy, A., Mackin, P., Wallenius, J., Corner, J., Keith, M., Schymik, G. and Arora, H. (2008) An interactive search method based on user preferences. Decision Analysis, 5(4), 203–229.
   Google Scholar
• Ryzhov, I.O., Powell, W.B. and Frazier, P. (2012) The knowledge gradient algorithm for a general class of online learning problems. Operations Research, 60(1), 180–195.
   Web of Science ®Google Scholar
• Sarkar, J. (1991) One-armed bandit problems with covariates. The Annals of Statistics, 19(4), 1978–2002.
   Web of Science ®Google Scholar
• Shively, T.S., Sager, T.W. and Walker, S.G. (2009) A Bayesian approach to non-parametric monotone function estimation. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(1), 159–175.
   Google Scholar
• Soons, P. and Feelders, A. (2014) Exploiting monotonicity constraints in active learning for ordinal classification, in Proceedings of the 2014 SIAM International Conference on Data Mining, Philadelphia, PA, pp. 659–667.
   Google Scholar
• Ulu, C. and Köksalan, M. (2001) An interactive procedure for selecting acceptable alternatives in the presence of multiple criteria. Naval Research Logistics, 48(7), 592–606.
   Web of Science ®Google Scholar
• Ulu, C. and Köksalan, M. (2014) An interactive approach to multicriteria sorting for quasiconcave value functions. Naval Research Logistics, 61(6), 447–457.
   Web of Science ®Google Scholar
• Wallenius, J., Dyer, J.S., Fishburn, P.C., Steuer, R.E., Zionts, S. and Deb, K. (2008) Multiple criteria decision making, multiattribute utility theory: Recent accomplishments and what lies ahead. Management Science, 54(7), 1336–1349.
   Web of Science ®Google Scholar
• Wang, H., Kou, G. and Peng, Y. (2021) Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending. Journal of the Operational Research Society, 72(4), 923–934.
   Web of Science ®Google Scholar
• Wilk, S., Słowiński, R., Michałowski, W. and Greco, S. (2005) Supporting triage of children with abdominal pain in the emergency room. European Journal of Operational Research, 160(3), 696–709.
   Web of Science ®Google Scholar
• Yang, Y. and Zhu, D. (2002) Randomized allocation with nonparametric estimation for a multi-armed bandit problem with covariates. The Annals of Statistics, 30(1), 100–121.
   Web of Science ®Google Scholar
• Zadrozny, B. and Elkan, C. (2001) Learning and making decisions when costs and probabilities are both unknown, in Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 204–213.
   Google Scholar
• Zopounidis, C. and Doumpos, M. (2002) Multicriteria classification and sorting methods: A literature review. European Journal of Operational Research 138(2), 229–246.
   Web of Science ®Google Scholar