D. Amaratunga, J. Cabrera, and Y. Lee, Enriched random forests, Bioinformatics, vol.24, issue.18, pp.2010-2014, 2008.
DOI : 10.1093/bioinformatics/btn356

Y. Amit and D. Geman, Shape Quantization and Recognition with Randomized Trees, Neural Computation, vol.1, issue.1, pp.1545-1588, 1997.
DOI : 10.1016/0031-3203(90)90098-6

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

S. Arlot and R. Genuer, Analysis of purely random forests bias, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01023596

L. Auret and C. Aldrich, Empirical comparison of tree ensemble variable importance measures, Chemometrics and Intelligent Laboratory Systems, vol.105, issue.2, pp.157-170, 2011.
DOI : 10.1016/j.chemolab.2010.12.004

M. Banerjee and I. W. Mckeague, Confidence sets for split points in decision trees. The Annals of Statistics, pp.543-574, 2007.

O. Barndorff-nielsen and M. Sobel, On the distribution of the number of admissible points in a vector random sample. Theory of Probability and Its Applications, pp.249-269, 1966.

S. Bernard, L. Heutte, and S. Adam, Forest-RK: A New Random Forest Induction Method
DOI : 10.1007/978-3-540-85984-0_52

URL : https://hal.archives-ouvertes.fr/hal-00436367

S. Bernard, S. Adam, and L. Heutte, Dynamic Random Forests, Pattern Recognition Letters, vol.33, issue.12, pp.1580-1586, 2012.
DOI : 10.1016/j.patrec.2012.04.003

URL : https://hal.archives-ouvertes.fr/hal-00710083

G. Biau, Analysis of a random forests model, Journal of Machine Learning Research, vol.13, pp.1063-1095, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00704947

G. Biau and L. Devroye, On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification, Journal of Multivariate Analysis, vol.101, issue.10, pp.2499-2518, 2010.
DOI : 10.1016/j.jmva.2010.06.019

URL : https://hal.archives-ouvertes.fr/hal-00559811

G. Biau and L. Devroye, Cellular tree classifiers, Electronic Journal of Statistics, vol.7, issue.0, pp.1875-1912, 2013.
DOI : 10.1214/13-EJS829

URL : https://hal.archives-ouvertes.fr/hal-00778520

G. Biau, L. Devroye, and G. Lugosi, Consistency of random forests and other averaging classifiers, Journal of Machine Learning Research, vol.9, pp.2015-2033, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00355368

G. Biau, F. Cérou, and A. Guyader, On the rate of convergence of the bagged nearest neighbor estimate, Journal of Machine Learning Research, vol.11, pp.687-712, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00911992

E. G. Bongiorno, A. Goia, E. Salinelli, and P. Vieu, Contributions in infinite-dimensional statistics and related topics, Contributions in infinite-dimensional statistics and related topics, 2014.
DOI : 10.15651/9788874887637

S. Boucheron, G. Lugosi, and P. Massart, Concentration inequalities: A nonasymptotic theory of independence, 2013.
DOI : 10.1093/acprof:oso/9780199535255.001.0001

URL : https://hal.archives-ouvertes.fr/hal-00794821

A. Boulesteix, S. Janitza, J. Kruppa, and I. R. König, Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol.12, issue.suppl 1, pp.493-507, 2012.
DOI : 10.1002/widm.1072

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

L. Breiman, Some infinity theory for predictor ensembles, 2000.

L. Breiman, Randomizing outputs to increase prediction accuracy, Machine Learning, pp.229-242, 2000.

L. Breiman, Random forests, Machine Learning, pp.5-32, 2001.

L. Breiman, Setting up, using, and understanding random forests V4.0. https, 2003.

L. Breiman, Consistency for a simple model of random forests, 2004.

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

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

B. Chen, D. Wild, and R. Guha, PubChem as a Source of Polypharmacology, Journal of Chemical Information and Modeling, vol.49, issue.9, pp.2044-2055, 2009.
DOI : 10.1021/ci9001876

S. Clémençon and N. Vayatis, Tree-Based Ranking Methods, IEEE Transactions on Information Theory, vol.55, issue.9, pp.4316-4336, 2009.
DOI : 10.1109/TIT.2009.2025558

S. Clémençon, M. Depecker, and N. Vayatis, Ranking forests, Journal of Machine Learning Research, vol.14, pp.39-73, 2013.

A. Criminisi, J. Shotton, and E. Konukoglu, Decision forests: A unified framework for classification , regression, density estimation, manifold learning and semi-supervised learning. Foundations and Trends in Computer Graphics and Vision, pp.81-227, 2011.

N. L. Crookston and A. O. Finley, yaImpute: An R package for kNN imputation, Journal of Statistical Software, vol.23, pp.1-16, 2008.

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

D. R. Cutler, T. C. Edwards-jr, K. H. Beard, A. Cutler, K. T. Hess et al., RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY, Ecology, vol.45, issue.11, pp.2783-2792, 2007.
DOI : 10.1016/S0034-4257(00)00145-0

A. Davies and Z. Ghahramani, The random forest kernel and other kernels for big data from random partitions, 2014.

H. Deng and G. Runger, Feature selection via regularized trees, The 2012 International Joint Conference on Neural Networks, pp.1-8, 2012.

H. Deng and G. Runger, Gene selection with guided regularized random forest, Pattern Recognition, vol.46, issue.12, pp.3483-3489, 2013.
DOI : 10.1016/j.patcog.2013.05.018

M. Denil, D. Matheson, and N. De-freitas, Consistency of online random forests, 2013.

C. Désir, S. Bernard, C. Petitjean, and L. Heutte, One class random forests, Pattern Recognition, vol.46, issue.12, pp.3490-3506, 2013.
DOI : 10.1016/j.patcog.2013.05.022

L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition, 1996.
DOI : 10.1007/978-1-4612-0711-5

R. Díaz-uriarte and S. Alvarez-de-andrés, Gene selection and classification of microarray data using random forest, BMC Bioinformatics, vol.7, 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.139-157, 2000.

T. G. Dietterich, Ensemble Methods in Machine Learning, Multiple classifier systems, pp.1-15, 2000.
DOI : 10.1007/3-540-45014-9_1

T. G. Dietterich and E. B. Kong, Machine learning bias, statistical bias, and statistical variance of decision tree algorithms, 1995.

B. Efron, The Jackknife, the Bootstrap and Other Resampling Plans, CBMS-NSF Regional Conference Series in Applied Mathematics, 1982.
DOI : 10.1137/1.9781611970319

T. Evgeniou, C. A. Micchelli, and M. Pontil, Learning multiple tasks with kernel methods, In Journal of Machine Learning Research, vol.6, pp.615-637, 2005.

F. Ferraty and P. Vieu, Nonparametric functional data analysis: theory and practice, 2006.

R. Genuer, Variance reduction in purely random forests, Journal of Nonparametric Statistics, vol.2, issue.3, pp.543-562, 2012.
DOI : 10.1007/978-0-387-84858-7

R. Genuer, J. Poggi, and C. Tuleau, Random forests: some methodological insights, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00340725

R. Genuer, J. Poggi, and C. Tuleau-malot, Variable selection using random forests, Pattern Recognition Letters, vol.31, issue.14, pp.2225-2236, 2010.
DOI : 10.1016/j.patrec.2010.03.014

URL : https://hal.archives-ouvertes.fr/hal-00755489

E. Geremia, B. H. Menze, and N. Ayache, Spatially Adaptive Random Forests, 2013 IEEE 10th International Symposium on Biomedical Imaging, pp.1332-1335, 2013.
DOI : 10.1109/ISBI.2013.6556781

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.405.8632

P. Geurts, D. Ernst, and L. Wehenkel, Extremely randomized trees, Machine Learning, pp.3-42, 2006.
DOI : 10.1007/s10994-006-6226-1

URL : https://hal.archives-ouvertes.fr/hal-00341932

W. Greblicki, A. Krzyzak, and M. Pawlak, Distribution-free pointwise consistency of kernel regression estimate. The Annals of Statistics, pp.1570-1575, 1984.

B. Gregorutti, B. Michel, and P. Saint-pierre, Correlation and variable importance in random forests, Statistics and Computing, vol.2, issue.1, 2013.
DOI : 10.1007/s11222-016-9646-1

URL : https://hal.archives-ouvertes.fr/hal-00879978

B. Gregorutti, B. Michel, and P. Saint-pierre, Grouped variable importance with random forests and application to multivariate functional data analysis, 2014.

I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines, Machine Learning, vol.46, issue.1/3, pp.389-422, 2002.
DOI : 10.1023/A:1012487302797

L. Györfi, M. Kohler, A. Krzy?ak, and H. Walk, A Distribution-Free Theory of Nonparametric Regression, 2002.
DOI : 10.1007/b97848

M. Hamza and D. Laroque, An empirical comparison of ensemble methods based on classification trees, Journal of Statistical Computation and Simulation, vol.24, issue.8, pp.629-643, 2005.
DOI : 10.1023/A:1010852229904

T. Hastie and R. Tibshirani, Generalized Additive Models, Statistical Science, vol.1, issue.3, pp.297-310, 1986.
DOI : 10.1214/ss/1177013604

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2009.

T. Ho, The random subspace method for constructing decision forests. Pattern Analysis and Machine Intelligence, pp.832-844, 1998.

L. Horváth and P. Kokoszka, Inference for functional data with applications
DOI : 10.1007/978-1-4614-3655-3

J. Howard and M. Bowles, The two most important algorithms in predictive modeling today, Strata Conference: Santa Clara, 2012.

T. Ishioka, Imputation of missing values for unsupervised data using the proximity in random forests, The Fifth International Conference on Mobile, Hybrid, and On-line Learning International Academy, Research, and Industry Association, pp.30-36, 2013.

H. Ishwaran, Variable importance in binary regression trees and forests, Electronic Journal of Statistics, vol.1, issue.0, pp.519-537, 2007.
DOI : 10.1214/07-EJS039

H. Ishwaran, The effect of splitting on random forests, Machine Learning, pp.1-44, 2013.
DOI : 10.1007/s10994-014-5451-2

H. Ishwaran and U. B. Kogalur, Consistency of random survival forests, Statistics & Probability Letters, vol.80, issue.13-14, pp.1056-1064, 2010.
DOI : 10.1016/j.spl.2010.02.020

H. Ishwaran, U. B. Kogalur, E. H. Blackstone, and M. S. Lauer, Random survival forests, The Annals of Applied Statistics, vol.2, issue.3, pp.841-860, 2008.
DOI : 10.1214/08-AOAS169

H. Ishwaran, U. B. Kogalur, X. Chen, and A. J. Minn, Random survival forests for highdimensional data. Statistical Analysis and Data Mining, The ASA Data Science Journal, vol.4, pp.115-132, 2011.

D. Jeffrey and G. Sanja, Simplified data processing on large clusters, Communications of the ACM, vol.51, pp.107-113, 2008.

A. Joly, P. Geurts, and L. Wehenkel, Random Forests with Random Projections of the Output Space for High Dimensional Multi-label Classification, Machine Learning and Knowledge Discovery in Databases, pp.607-622, 2014.
DOI : 10.1007/978-3-662-44848-9_39

A. Kleiner, A. Talwalkar, P. Sarkar, and M. I. Jordan, A scalable bootstrap for massive data, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.90, issue.4, 2012.
DOI : 10.1111/rssb.12050

E. Konukoglu and M. Ganz, Approximate false positive rate control in selection frequency for random forest, 2014.

A. Kyrillidis and A. Zouzias, Non-uniform feature sampling for decision tree ensembles, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4548-4552, 2014.
DOI : 10.1109/ICASSP.2014.6854463

B. Lakshminarayanan, D. M. Roy, and Y. W. Teh, Mondrian forests: Efficient online random forests, 2014.

P. Latinne, O. Debeir, and C. Decaestecker, Limiting the Number of Trees in Random Forests
DOI : 10.1007/3-540-48219-9_18

B. Laurent and P. Massart, Adaptive estimation of a quadratic functional by model selection. The Annals of Statistics, pp.1302-1338, 2000.

A. Liaw and M. Wiener, Classification and regression by randomforest, pp.18-22, 2002.

Y. Lin and Y. Jeon, Random Forests and Adaptive Nearest Neighbors, Journal of the American Statistical Association, vol.101, issue.474, pp.578-590, 2006.
DOI : 10.1198/016214505000001230

G. Louppe, L. Wehenkel, A. Sutera, and P. Geurts, Understanding variable importances in forests of randomized trees, Advances in Neural Information Processing Systems, pp.431-439, 2013.

P. Mahé, N. Ueda, T. Akutsu, J. Perret, and J. Vert, Graph Kernels for Molecular Structure???Activity Relationship Analysis with Support Vector Machines, Journal of Chemical Information and Modeling, vol.45, issue.4, pp.939-951, 2005.
DOI : 10.1021/ci050039t

L. Meier, S. Van-de-geer, and P. Bühlmann, High-dimensional additive modeling. The Annals of Statistics, pp.3779-3821, 2009.

N. Meinshausen, Quantile regression forests, Journal of Machine Learning Research, vol.7, pp.983-999, 2006.

N. Meinshausen, . Forest, and . Garrote, Forest Garrote, Electronic Journal of Statistics, vol.3, issue.0, pp.1288-1304, 2009.
DOI : 10.1214/09-EJS434

L. Mentch and G. Hooker, Ensemble trees and clts: Statistical inference for supervised learning, 2014.

L. Mentch and G. Hooker, A novel test for additivity in supervised ensemble learners, 2014.

E. A. Nadaraya, On estimating regression. Theory of Probability and Its Applications, pp.141-142, 1964.

K. K. Nicodemus and J. D. Malley, Predictor correlation impacts machine learning algorithms: implications for genomic studies, Bioinformatics, vol.25, issue.15, pp.1884-1890, 2009.
DOI : 10.1093/bioinformatics/btp331

J. Poggi and C. Tuleau, Classification supervisée en grande dimension. application à l'agrément de conduite automobile, pp.41-60, 2006.

A. M. Prasad, L. R. Iverson, and A. Liaw, Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction, Ecosystems, vol.17, issue.6, pp.181-199, 2006.
DOI : 10.1007/s10021-005-0054-1

Y. Qi, Ensemble Machine Learning, chapter Random forest for bioinformatics, pp.307-323, 2012.

S. S. Qian, R. S. King, and C. J. Richardson, Two statistical methods for the detection of environmental thresholds, Ecological Modelling, vol.166, issue.1-2, pp.87-97, 2003.
DOI : 10.1016/S0304-3800(03)00097-8

J. O. Ramsay and B. W. Silverman, Functional Data Analysis, 2005.

A. Rieger, T. Hothorn, and C. Strobl, Random forests with missing values in the covariates, 2010.

G. Rogez, J. Rihan, S. Ramalingam, C. Orrite, and P. H. Torr, Randomized trees for human pose detection, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587617

A. Saffari, C. Leistner, J. Santner, M. Godec, and H. Bischof, On-line Random Forests, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp.1393-1400, 2009.
DOI : 10.1109/ICCVW.2009.5457447

E. Scornet, On the asymptotics of random forests. arXiv:1409, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01061506

E. Scornet, Random Forests and Kernel Methods, IEEE Transactions on Information Theory, vol.62, issue.3, 2015.
DOI : 10.1109/TIT.2016.2514489

URL : https://hal.archives-ouvertes.fr/hal-01116158

E. Scornet, G. Biau, and J. Vert, Consistency of random forests, The Annals of Statistics, vol.43, issue.4, 2015.
DOI : 10.1214/15-AOS1321SUPP

URL : https://hal.archives-ouvertes.fr/hal-00990008

E. Scornet, G. Biau, and J. Vert, Consistency of random forests. The Annals of Statistics, pp.1716-1741, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00990008

J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio et al., Real-time human pose recognition in parts from single depth images, IEEE Conference on Computer Vision and Pattern Recognition, pp.1297-1304, 2011.

C. J. Stone, Consistent nonparametric regression. The Annals of Statistics, pp.595-645, 1977.

C. J. Stone, Optimal rates of convergence for nonparametric estimators. The Annals of Statistics, pp.1348-1360, 1980.

C. J. Stone, Optimal global rates of convergence for nonparametric regression. The Annals of Statistics, pp.1040-1053, 1982.

C. J. Stone, Additive regression and other nonparametric models. The Annals of Statistics, pp.689-705, 1985.

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

V. Svetnik, A. Liaw, C. Tong, J. C. Culberson, R. P. Sheridan et al., Random Forest:??? A Classification and Regression Tool for Compound Classification and QSAR Modeling, Journal of Chemical Information and Computer Sciences, vol.43, issue.6, pp.1947-1958, 2003.
DOI : 10.1021/ci034160g

L. Tolo?i and T. Lengauer, Classification with correlated features: unreliability of feature ranking and solutions, Bioinformatics, vol.27, issue.14, pp.1986-1994, 2011.
DOI : 10.1093/bioinformatics/btr300

A. K. Truong, Fast Growing and Interpretable Oblique Trees via Logistic Regression Models, 2009.

M. Van-der-laan, E. C. Polley, and A. E. Hubbard, Super Learner, Statistical Applications in Genetics and Molecular Biology, vol.6, issue.1, 2007.
DOI : 10.2202/1544-6115.1309

W. Aad, . Van, J. A. Vaart, and . Wellner, Weak Convergence and Empirical Processes : With Applications to Statistics, 1996.

H. Varian, Big Data: New Tricks for Econometrics, Journal of Economic Perspectives, vol.28, issue.2, pp.3-28
DOI : 10.1257/jep.28.2.3

S. Wager, Asymptotic theory for random forests, 2014.

S. Wager, T. Hastie, and B. Efron, Standard errors for bagged predictors and random forests, 2013.

S. Wager, T. Hastie, and B. Efron, Confidence intervals for random forests: The jackknife and the infinitesimal jackknife, Journal of Machine Learning Research, vol.15, pp.1625-1651, 2014.

G. S. Watson, Smooth regression analysis Sankhy¯ a: The Indian Journal of Statistics, Series A, pp.359-372, 1964.

S. J. Winham, R. R. Freimuth, and J. M. Biernacka, A weighted random forests approach to improve predictive performance. Statistical Analysis and Data Mining, The ASA Data Science Journal, vol.6, pp.496-505, 2013.

Y. Yamanishi, E. Pauwels, H. Saigo, and V. Stoven, Extracting Sets of Chemical Substructures and Protein Domains Governing Drug-Target Interactions, Journal of Chemical Information and Modeling, vol.51, issue.5, pp.1183-1194, 2011.
DOI : 10.1021/ci100476q

D. Yan, A. Chen, and M. I. Jordan, Cluster Forests, Computational Statistics & Data Analysis, vol.66, pp.178-192, 2013.
DOI : 10.1016/j.csda.2013.04.010

F. Yang, J. Wang, and G. Fan, Kernel induced random survival forests, 2010.

Z. Yi, S. Soatto, M. Dewan, and Y. Zhan, Information Forests, 2012 Information Theory and Applications Workshop, pp.143-146, 2012.
DOI : 10.1109/ITA.2012.6181810

R. Zhu, D. Zeng, and M. R. Kosorok, Reinforcement Learning Trees, Journal of the American Statistical Association, vol.7, issue.512, 2012.
DOI : 10.1080/01621459.2011.637468