F. Badra, Reasoning with Co-variations, Artificial Intelligence: Methodology, Systems, and Applications -17th International Conference, AIMSA. Varna, Bulgaria, 2016.

M. Bounhas, H. Prade, and G. Richard, Analogy-based classifiers for nominal or numerical data, International Journal of Approximate Reasoning, vol.91, pp.36-55, 2017.

S. Craw, N. Wiratunga, and R. C. Rowe, Learning adaptation knowledge to improve case-based reasoning, Artificial Intelligence, vol.170, pp.1175-1192, 2006.

M. D'aquin, F. Badra, S. Lafrogne, J. Lieber, A. Napoli et al., Case base mining for adaptation knowledge acquisition, IJCAI International Joint Conference on Artificial Intelligence, pp.750-755, 2007.

T. R. Davis and S. J. Russell, A Logical Approach to Reasoning by Analogy, IJCAI International Joint Conference on Artificial Intelligence, 1987.

S. De-amo, M. S. Diallo, C. T. Diop, A. Giacometti, D. Li et al., Contextual preference mining for user profile construction, Information Systems, vol.49, pp.182-199, 2015.

J. Derrac and S. Schockaert, Inducing semantic relations from conceptual spaces: A data-driven approach to plausible reasoning, Artificial Intelligence, vol.228, pp.66-94, 2015.

B. Fuchs, J. Lieber, A. Mille, and A. Napoli, Differential adaptation: An operational approach to adaptation for solving numerical problems with CBR. Knowledge-Based Systems, vol.68, pp.103-114, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01101145

,

H. Gust, U. Krumnack, K. Kühnberger, and A. Schwering, Analogical Reasoning: A Core of Cognition, KI -Künstliche Intelligenz, vol.22, issue.1, pp.8-12, 2008.

K. J. Hammond, CHEF: A Model of Case-based Planning, AAAI Proceedings, pp.267-271, 1986.

K. Hanney and M. T. Keane, The adaptation knowledge bottleneck: How to ease it by learning from cases, International Conference on Case-Based Reasoning, vol.1266, pp.359-370, 1997.

N. Hug, H. Prade, G. Richard, and M. Serrurier, Analogical classifiers: A theoretical perspective, 22nd European Conference on Artificial Intelligence -ECAI, vol.285, pp.689-697, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01782594

E. Hüllermeier, Possibilistic instance-based learning, Artificial Intelligence, vol.148, issue.1-2, pp.335-383, 2003.

V. Jalali, D. Leake, and N. Forouzandehmehr, Learning and applying case adaptation rules for classification: An ensemble approach, IJCAI International Joint Conference on Artificial Intelligence, pp.4874-4878, 2017.

J. Lieber, Application of the Revision Theory to Adaptation in Case-Based Reasoning: The Conservative Adaptation, ICCBR proceedings, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00189626

J. Lieber and A. Napoli, Correct and Complete Retrieval for Case-Based Problem-Solving, ECAI. pp, pp.68-72, 1998.

D. Mcsherry, Demand-driven discovery of adaptation knowledge, IJCAI, pp.222-227, 1999.

L. Miclet, S. Bayoudh, and A. Delhay, Analogical Dissimilarity: Definition, Algorithms and Two Experiments in Machine Learning, Journal of Artificial Intelligence Research, vol.32, pp.793-824, 2008.

T. M. Mitchell, Machine Learning, 1983.

L. R. Novick and K. J. Holyoak, Journal of experimental psychology. Learning, memory, and cognition, vol.17, pp.398-415, 1991.

L. R. Novick, Analogical Transfer, Problem Similarity, and Expertise, Journal of Experimental Psychology: Learning, Memory, and Cognition, vol.14, issue.3, pp.510-529, 1988.

S. Ontañón and E. Plaza, On Knowledge Transfer in Case-Based Inference, pp.312-326, 2012.

P. K. Paritosh and M. E. Klenk, Cognitive processes in quantitative estimation: analogical anchors and causal adjustment, Proceedings of the 28th Annual Conference of the Cognitive Science Society (CogSci-06, 2006.

M. Pirlot, H. Prade, and G. Richard, Completing Preferences by Means of Analogical Proportions, Modeling Decisions for Artificial Intelligence, vol.4617, pp.318-329, 2016.

H. Prade and G. Richard, Reasoning with Logical Proportions, Principles of Knowledge Representation and Reasoning: Proceedings of the Twelfth International Conference, pp.545-555, 2010.

H. Prade and G. Richard, Analogical inequalities, Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 14th European Conference, ECSQARU'17, pp.3-9, 2017.

F. S. Robert, Measurement Theory, 1985.

A. Tversky, Features of Similarity, Readings in Cognitive Science: A Perspective from Psychology and Artificial Intelligence, pp.290-302, 2013.

Y. Y. Yao, Y. Suzuki, S. Ovaska, T. Furuhashi, and R. Roy, Qualitative Similarity, Soft Computing in Industrial Applications, pp.339-348, 2000.

J. ?abkar, I. Bratko, and J. Dem?ar, Extracting qualitative relations from categorical data, Artificial Intelligence, vol.239, pp.54-69, 2016.

,