U. Alon, N. Barkai, D. A. Notterman, K. Gish, S. Ybarra et al., Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays, Proceedings of the National Academy of Sciences, vol.96, issue.12, 1999.
DOI : 10.1073/pnas.96.12.6745

A. A. Alizadeh, Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling, Nature, vol.303, issue.6769, pp.503-511, 2000.
DOI : 10.1038/35000501

K. J. Archer and R. V. Kimes, Empirical characterization of random forest variable importance measures, Computational Statistics & Data Analysis, vol.52, issue.4, pp.2249-2260, 2008.
DOI : 10.1016/j.csda.2007.08.015

B. Ishak, A. Ghattas, and B. , Sélection de variables en classification binaire : comparaisons et application aux données de biopuces, 2008.

G. Biau, L. Devroye, and G. Lugosi, Consistency of random forests and other averaging classifiers, Journal of Machine Learning Research, vol.9, pp.2039-2057, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00355368

L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification And Regression Trees, 1984.

L. Breiman, Bagging predictors, Machine Learning, pp.123-140, 1996.
DOI : 10.1007/BF00058655

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

L. Breiman, Consistency for a simple model of Random Forests, 2004.

L. Breiman and A. Cutler, Random Forests, 2005.

P. Bühlmann and B. Yu, Analyzing Bagging. The Annals of Statistics, pp.927-961, 2002.

N. Cheze, J. M. Poggi, and B. Portier, Partial and Recombined Estimators for Nonlinear Additive Models, Statistical Inference for Stochastic Processes, vol.6, issue.2, pp.155-197, 2003.
DOI : 10.1023/A:1023940117323

A. Cutler and G. Zhao, Pert -Perfect random tree ensembles, Computing Science and Statistics, vol.33, pp.490-497, 2001.

D. ´-iaz-uriarte, R. Alvarez-de-andrés, and S. , Gene Selection and classification of microarray data using random forest, BMC Bioinformatics, vol.7, issue.3, pp.1-13, 2006.

T. Dietterich, An experimental comparison of three methods for constructing ensembles of decision trees : Bagging, Boosting and randomization, Machine Learning, pp.1-22, 1999.

T. Dietterich, Ensemble Methods in Machine Learning, Lecture Notes in Computer Science, vol.1857, pp.1-15, 2000.
DOI : 10.1007/3-540-45014-9_1

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, Annals of Statistics, vol.32, issue.2, pp.407-499, 2004.

J. Fan and J. Lv, Sure independence screening for ultrahigh dimensional feature space, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.36, issue.5, pp.849-911, 2008.
DOI : 10.1111/j.1467-9868.2008.00674.x

I. Guyon, J. Weston, S. Barnhill, and V. N. Vapnik, Gene selection for cancer classification using support vector machines, Machine Learning, pp.1-3389, 2002.

I. Guyon and A. Elisseff, An introduction to variable and feature selection, Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003.

T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek et al., Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring, Science, vol.286, issue.5439, pp.531-537, 1999.
DOI : 10.1126/science.286.5439.531

U. Grömping, Estimators of Relative Importance in Linear Regression Based on Variance Decomposition, The American Statistician, vol.61, issue.2, pp.139-147, 2007.
DOI : 10.1198/000313007X188252

U. Grömping, Relative Importance for Linear Regression in R: The Package relaimpo, Journal of Statistical Software, vol.17, issue.1, 2006.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2001.

T. K. Ho, The random subspace method for constructing decision forests, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.20, issue.8, pp.832-844, 1998.

J. Khan, J. S. Wei, M. Ringner, L. H. Saal, M. Ladanyi et al., Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks, Nature Medicine, vol.7, issue.6, pp.673-679, 2001.
DOI : 10.1038/89044

R. Kohavi and G. H. John, Wrappers for feature subset selection, Artificial Intelligence, vol.97, issue.1-2, pp.273-324, 1997.
DOI : 10.1016/S0004-3702(97)00043-X

A. Liaw and M. Wiener, Classification and Regression by random- Forest, pp.18-22, 2002.

M. Y. Park and T. Hastie, -regularization path algorithm for generalized linear models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.67, issue.4, pp.659-677, 2007.
DOI : 10.1073/pnas.082099299
URL : https://hal.archives-ouvertes.fr/hal-00458708

J. M. Poggi and C. Tuleau, Classification supervisée en grande dimension . ApplicationàApplication`Applicationà l'agrément de conduite automobile, pp.39-58, 2006.

S. L. Pomeroy, P. Tamayo, M. Gaasenbeek, L. M. Sturla, M. Angelo et al., Prediction of central nervous system embryonal tumour outcome based on gene expression, Nature, vol.415, issue.6870, pp.436-442, 2002.
DOI : 10.1038/415436a

A. Rakotomamonjy, Variable selection using SVM-based criteria, Journal of Machine Learning Research, vol.3, pp.1357-1370, 2003.

S. Ramaswamy, K. N. Ross, E. S. Lander, and T. R. Golub, A molecular signature of metastasis in primary solid tumors, Nature Genetics, vol.5, issue.1, pp.49-54, 2003.
DOI : 10.1073/pnas.95.25.14863

D. T. Ross, U. Scherf, M. B. Eisen, C. M. Perou, C. Rees et al., Systematic variation in gene expression patterns in human cancer cell lines, Nature Genetics, vol.59, issue.3, pp.227-235, 2000.
DOI : 10.1126/science.278.5342.1481

D. Singh, P. G. Febbo, K. Ross, D. G. Jackson, J. Manola et al., Gene expression correlates of clinical prostate cancer behavior, Cancer Cell, vol.1, issue.2, pp.203-209, 2002.
DOI : 10.1016/S1535-6108(02)00030-2

C. Strobl, A. Boulesteix, A. Zeileis, and T. Hothorn, Bias in random forest variable importance measures: illustrations, sources and a solution, BMC Bioinformatics, vol.8, issue.1, p.25, 2007.
DOI : 10.1186/1471-2105-8-25

C. Strobl, A. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis, Conditional Variable Importance for Random Forests, BMC Bioinformatics, vol.9, issue.1, p.307, 2008.
DOI : 10.1186/1471-2105-9-307
URL : http://doi.org/10.1186/1471-2105-9-307

L. J. Van-'t-veer, H. Dai, M. J. Van-de-vijver, Y. D. He, A. A. Hart et al., Gene expression profiling predicts clinical outcome of breast cancer, Nature, vol.415, issue.6871, pp.530-536, 2002.
DOI : 10.1038/415530a

J. Weston, A. Elisseff, B. Schoelkopf, and M. Tipping, Use of the zero norm with linear models and kernel methods, Journal of Machine Learning Research, vol.3, pp.1439-1461, 2003.