J. R. Quinlan and C. , 5: programs for machine learning, 2014.

S. L. Crawford, Extensions to the CART algorithm, International Journal of Man-Machine Studies, vol.31, issue.2, pp.197-217, 1989.
DOI : 10.1016/0020-7373(89)90027-8

P. E. Utgoff, N. C. Berkman, and J. A. Clouse, Decision tree induction based on efficient tree restructuring, Machine Learning, pp.5-44, 1997.

P. Domingos and G. Hulten, Mining high-speed data streams, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '00, pp.71-80, 2000.
DOI : 10.1145/347090.347107

M. H. Degroot, Optimal statistical decisions, 2005.
DOI : 10.1002/0471729000

L. Kocsis and C. Szepesvári, Bandit Based Monte-Carlo Planning, Machine Learning: ECML 2006, pp.282-293, 2006.
DOI : 10.1007/11871842_29

P. Auer, N. Cesa-bianchi, and P. Fischer, Finite-time analysis of the multiarmed bandit problem, Machine learning, 2002.

J. Gama, P. Medas, and R. Rocha, Forest trees for on-line data, Proceedings of the 2004 ACM symposium on Applied computing , SAC '04, pp.632-636, 2004.
DOI : 10.1145/967900.968033

E. Kaufmann, O. Cappé, and A. Garivier, On bayesian upper confidence bounds for bandit problems, International Conference on Artificial Intelligence and Statistics, pp.592-600, 2012.

B. Settles, Active learning literature survey University of Wisconsin, pp.55-66, 2010.

K. Yu, J. Bi, and V. Tresp, Active learning via transductive experimental design, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.1081-1088, 2006.
DOI : 10.1145/1143844.1143980

Q. Gu, T. Zhang, J. Han, and C. H. Ding, Selective labeling via error bound minimization, Advances in Neural Information Processing Systems, pp.323-331, 2012.

T. Collet and O. Pietquin, Active learning for classification: An optimistic approach, 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), pp.1-8, 2014.
DOI : 10.1109/ADPRL.2014.7010610

URL : https://hal.archives-ouvertes.fr/hal-01107508

A. Carpentier, De l'échantillonage optimal en grande et petite dimension, 2012.

M. Lichman, UCI machine learning repository, 2013.

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324