H. Almuallim and T. G. Dietterich, Learning Boolean concepts in the presence of many irrelevant features, Artificial Intelligence, vol.69, issue.1-2, pp.279-305, 1994.
DOI : 10.1016/0004-3702(94)90084-1

A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth, Occam's Razor, Information Processing Letters, vol.24, issue.6, pp.377-380, 1987.
DOI : 10.1016/0020-0190(87)90114-1

. Bessì-ere, Interprétation versus description (i) : Proposition pour une théorie probabiliste des systèmes cognitifs sensorimoteurs, Intellectica, 1999.

O. Gaudoin, Méthodes statistiques pour l'ingénieur. grenoble, france, 2002.

E. Edwin and . Ghiselli, Theory of Psychological Measurement, 1964.

M. Hall, Correlation-based feature selection for machine learning, 1998.

A. Mark and . Hall, Correlation-based feature selection for discrete and numeric class machine learning, Proc. 17th International Conf. on Machine Learning, pp.359-366, 2000.

D. Heckerman, D. Geiger, and D. M. Chickering, Learning bayesian networks : The combination of knowledge and statistical data, In KDD Workshop, pp.85-96, 1994.

A. Elisseeff and I. Guyon, An introduction to variable and feature selection, Journal of Machine Learning Research, 2003.

G. H. John, R. Kohavi, and K. Pfleger, Irrelevant Features and the Subset Selection Problem, International Conference on Machine Learning, pp.121-129, 1994.
DOI : 10.1016/B978-1-55860-335-6.50023-4
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.3875

M. Sir, A. Kendall, and . Stewart, The Advanced Theory of Statistics, 1977.

D. Koller and M. Sahami, Toward optimal feature selection, International Conference on Machine Learning, pp.284-292, 1996.

S. Kullback and L. , On information and suoeciencyBessì ere P. and Mazer E. Bayesian robots programming, Diard J. Lebeltel O. Autonomous Robot, vol.2214, p.7986, 1951.

H. Liu and R. Setiono, Chi2 : Feature selection and discretization of numeric attributes, 1995.

H. Liu and L. Yu, Feature selection for data mining, 2002.

D. Margartitis and S. Thrun, A bayesian multiresolution independence test for continuous variables, Uncertainty in Artificial Intelligence : Proceedings of the Seventeenth Conference (UAI-2001), pp.346-353, 2001.

O. Lebeltel, Programmation bayésienne des robots. thèse de l'institut national polytechnique de grenoble, france, 1999.
DOI : 10.3166/ria.18.261-298
URL : https://hal.inria.fr/inria-00182069/file/BRP_RIA.pdf

O. Ritthoff, A hybrid approach to feature selection and generation using an ea, 2002.

R. David and . Wolf, Mutual information as a bayesian measure of independence, 1994.

J. Yang and V. Honavar, Feature subset selection using a genetic algorithm, IEEE Intelligent Systems, vol.13, issue.2, pp.44-49, 1998.
DOI : 10.1109/5254.671091
URL : http://archives.cs.iastate.edu/documents/disk0/00/00/01/47/00000147-01/TR97-02.pdf

M. Zaffalon and M. Hutter, Robust feature selection by mutual information distributions, 2002.