D. Aha, D. Kibler, and M. K. Albert, Instance-based learning algorithms, Machine Learning, vol.57, issue.1, pp.37-66, 1991.
DOI : 10.1145/1968.1972
URL : https://link.springer.com/content/pdf/10.1007%2FBF00153759.pdf

R. R. Bouckaert, Bayesian Belief Networks: from Construction to Inference, 1995.

W. W. Cohen, Fast Effective Rule Induction, Proceedings of the 12th International Conference on Machine Learning, pp.115-123, 1995.
DOI : 10.1016/B978-1-55860-377-6.50023-2
URL : http://www.cs.iitm.ernet.in/~cs672/Lectures/ml-95-ripper.pdf

G. Holmes, M. Hall, and E. Prank, Generating Rule Sets from Model Trees. Advanced Topics in Artificial Intelligence, pp.1-12, 1747.
DOI : 10.1007/3-540-46695-9_1
URL : http://www.cs.waikato.ac.nz/~ml/publications/1999/ajc.pdf

R. C. Holte, Very simple classification rules perform well on most commonly used datasets, Machine Learning, vol.11, issue.1, pp.63-91, 1993.
DOI : 10.1023/A:1022631118932

J. R. Quinlan, C4.5: Programs For Machine Learning, 1993.

L. Breiman, J. H. Fried, R. A. Olshen, and C. J. Stone, Classification and regression trees, 1984.

R. O. Duda and P. Hart, Pattern classification and Scene Analysis, 1973.

V. Vapnik and A. Lerner, Pattern Recognition Using Generalized Portrait Method, pp.774-780, 1963.

L. Breiman, Bagging predictors, Machine Learning, pp.123-140, 1984.
DOI : 10.2307/1403680

Y. Freund and R. E. Schapire, Experiments with a new boosting algorithm, Thirteenth International Conference on Machine Learning, pp.148-156, 1996.

J. A. Fraile, Y. De-paz, J. Bajo, J. Francisco-de, and P. , Belén Pérez Lancho: Context-aware multiagent system: Planning home care tasks, Knowl. Inf. Syst, vol.40, issue.1, pp.171-203, 2014.
DOI : 10.1007/s10115-013-0635-9

S. Rodríguez, J. Francisco-de-paz, G. Villarrubia, C. Zato, J. Bajo et al., Multi-Agent Information Fusion System to manage data from a WSN in a residential home, Information Fusion, vol.23, pp.43-57, 2015.
DOI : 10.1016/j.inffus.2014.03.003

J. Abellán, G. López, and J. De-oña, Analysis of traffic accident severity using Decision Rules via Decision Trees, Expert Systems with Applications, vol.40, issue.15, pp.40-6047, 2013.
DOI : 10.1016/j.eswa.2013.05.027

R. C. Barros, P. A. Jaskowiak, R. Cerri, and A. C. De-carvalho, A framework for bottom-up induction of oblique decision trees, Neurocomputing, vol.135, issue.5, pp.135-138, 2014.
DOI : 10.1016/j.neucom.2013.01.067

M. Ture, F. Tokatli, and I. Kurt, Using Kaplan???Meier analysis together with decision tree methods (C&RT, CHAID, QUEST, C4.5 and ID3) in determining recurrence-free survival of breast cancer patients, Expert Systems with Applications, vol.36, issue.2, pp.2017-2026, 2009.
DOI : 10.1016/j.eswa.2007.12.002

F. Juan, J. De-paz, V. Bajo, J. M. Vera, and . Corchado, MicroCBR: A case-based reasoning architecture for the classification of microarray data, Applied Soft Computing, issue.8, pp.11-4496, 2011.

K. Seung-hyun-lee, S. Yang, and . Cho, Integrated modular Bayesian networks with selective inference for context-aware decision making, Neurocomputing, vol.163, issue.2, pp.38-46, 2015.

J. Francisco-de-paz, J. Bajo, A. González, S. Rodríguez, and M. Juan, Combining case-based reasoning systems and support vector regression to evaluate the atmosphere???ocean interaction, Knowledge and Information Systems, vol.25, issue.9, pp.155-177, 2012.
DOI : 10.1080/01431160410001675541

G. Villarrubia, J. F. De-paz, D. Pelki, S. Fernando-de-la-prieta, and . Omatu, Virtual organization with fusion knowledge in odor classification, Neurocomputing, vol.231, 2016.
DOI : 10.1016/j.neucom.2016.05.106