T. Agarwal, R. Graepel, S. Herbrich, D. Har-peled, and . Roth, Generalization bounds for the area under the ROC curve, J. Mach. Learn. Res, vol.6, pp.393-425, 2005.

Y. Amit and D. Geman, Shape quantization and recognition with randomized trees, Neural Computation, vol.9, pp.1545-1587, 1997.
DOI : 10.1162/neco.1997.9.7.1545
URL : http://www.wisdom.weizmann.ac.il/~vision/courses/2003_2/shape.pdf

S. Arlot, Model selection by resampling techniques, Electronic Journal of Statistics, vol.3, pp.557-624, 2009.
DOI : 10.1214/08-ejs196
URL : https://doi.org/10.1214/08-ejs196

M. S. Bansal and D. Fernandez-baca, Computing distances between partial rankings, Information Processing Letters, vol.109, pp.238-241, 2009.
DOI : 10.1016/j.ipl.2008.10.010

J. P. Barthélémy and B. Montjardet, The median procedure in cluster analysis and social choice theory, Mathematical Social Sciences, vol.1, pp.235-267, 1981.

N. Baskiotis, S. Clémençon, M. Depecker, and N. Vayatis, R-implementation of the TreeRank algorithm, 2009.

N. Betzler, M. R. Fellows, J. Guo, R. Niedermeier, and F. A. Rosamond, Computing kemeny rankings, parameterized by the average kt-distance, Proceedings of the 2nd International Workshop on Computational Social Choice, 2008.

G. Biau, L. Devroye, and G. Lugosi, Consistency of Random Forests, J. Mach. Learn. Res, vol.9, pp.2039-2057, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00990008

L. Breiman, Bagging predictors, Machine Learning, vol.26, pp.123-140, 1996.
DOI : 10.1007/bf00058655
URL : https://link.springer.com/content/pdf/10.1007%2FBF00058655.pdf

L. Breiman, Random forests, Machine Learning, vol.45, pp.5-32, 2001.

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

I. Charon and O. Hudry, Lamarckian genetic algorithms applied to the aggregation of preferences, Annals of Operations Research, vol.80, pp.281-297, 1998.

S. Clémençon and N. Vayatis, Ranking the best instances, Journal of Machine Learning Research, vol.8, pp.2671-2699, 2007.

S. Clémençon and N. Vayatis, Empirical performance maximization based on linear rank statistics, NIPS, vol.3559, pp.1-15, 2009.

S. Clémençon and N. Vayatis, Overlaying classifiers: a practical approach to optimal scoring, 2009.

S. Clémençon and N. Vayatis, Tree-based ranking methods, IEEE Transactions on Information Theory, vol.55, issue.9, pp.4316-4336, 2009.

S. Clémençon, G. Lugosi, and N. Vayatis, Ranking and scoring using empirical risk minimization, Proceedings of COLT, 2005.

S. Clémençon, G. Lugosi, and N. Vayatis, Ranking and empirical risk minimization of U-statistics, The Annals of Statistics, vol.36, issue.2, pp.844-874, 2008.

S. Clémençon, M. Depecker, and N. Vayatis, Bagging ranking trees, Proceedings of ICMLA'09, pp.658-663, 2009.

S. Clémençon, M. Depecker, and N. Vayatis, AUC-optimization and the two-sample problem, Proceedings of NIPS'09, 2010.

S. Clémençon, M. Depecker, and N. Vayatis, Adaptive partitioning schemes for bipartite ranking, Machine Learning, vol.83, pp.31-69, 2011.

W. W. Cohen, R. E. Schapire, and Y. Singer, Learning to order things, Journal of Artificial Intelligence Research, vol.10, pp.243-270, 1999.

V. De-la-pena and E. Giné, Decoupling: from Dependence to Independence, 1999.

M. M. Deza and E. Deza, Encyclopedia of Distances, 2009.

P. Diaconis, A generalization of spectral analysis with application to ranked data, The Annals of Statistics, vol.17, issue.3, pp.949-979, 1989.

R. M. Dudley, Uniform Central Limit Theorems, 1999.

C. Dwork, R. Kumar, M. Naor, and D. Sivakumar, Rank aggregation methods for the Web, Proceedings of the 10th International WWW Conference, pp.613-622, 2001.

J. P. Egan, Signal Detection Theory and ROC Analysis, 1975.

R. Fagin, R. Kumar, M. Mahdian, D. Sivakumar, and E. Vee, Comparing and aggregating rankings with ties, Proceedings of the 12-th WWW Conference, pp.366-375, 2003.

R. Fagin, R. Kumar, M. Mahdian, D. Sivakumar, and E. Vee, Comparing partial rankings, SIAM J. Discrete Mathematics, vol.20, issue.3, pp.628-648, 2006.
DOI : 10.1137/05063088x

P. Fishburn, The Theory of Social Choice, 1973.

, Probability Models and Statistical Analyses for Ranking Data, 1993.

Y. Freund, R. D. Iyer, R. E. Schapire, and Y. Singer, An efficient boosting algorithm for combining preferences, Journal of Machine Learning Research, vol.4, pp.933-969, 2003.

J. Friedman and P. Hall, On bagging and non-linear estimation, Journal of Statistical Planning and Inference, vol.137, issue.3, pp.669-683, 2007.

Y. Grandvalet, Bagging equalizes influence, Machine Learning, vol.55, pp.251-270, 2004.
DOI : 10.1023/b:mach.0000027783.34431.42
URL : https://link.springer.com/content/pdf/10.1023%2FB%3AMACH.0000027783.34431.42.pdf

T. Hastie and R. Tibshirani, Generalized Additive Models, 1990.

J. M. Hilbe, Logistic Regression Models, 2009.

J. Howie, Hyperbolic groups, Groups and Applications, pp.137-160, 2000.

O. Hudry, Computation of median orders: complexity results, Proceedings of the Workshop on Computer Science and Decision Theory, vol.3, pp.179-214, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00018369

O. Hudry, NP-hardness results for the aggregation of linear orders into median orders, Ann. Oper. Res, vol.163, pp.63-88, 2008.

E. Hüllermeier, J. Fürnkranz, W. Cheng, and K. Brinker, Label ranking by learning pairwise preferences, Artificial Intelligence, vol.172, pp.1897-1917, 2008.

I. Ilyas, W. Aref, and A. Elmagarmid, Joining ranked inputs in practice, Proceedings of the 28th International Conference on Very Large Databases, pp.950-961, 2002.
DOI : 10.1016/b978-155860869-6/50094-9

J. G. Kemeny, Mathematics without numbers, Daedalus, issue.88, pp.571-591, 1959.

M. G. Kendall, The treatment of ties in ranking problems, Biometrika, issue.33, pp.239-251, 1945.

A. Klementiev, D. Roth, K. Small, and I. Titov, Unsupervised rank aggregation with domain-specific expertise, IJCAI'09: Proceedings of the 21st International Joint Conference on Artifical Intelligence, pp.1101-1106, 2009.
DOI : 10.1145/1390156.1390216

M. Laguna, R. Marti, and V. Campos, Intensification and diversification with elite tabu search solutions for the linear ordering problem, Computers and Operations Research, vol.26, issue.12, pp.1217-1230, 1999.
DOI : 10.1016/s0305-0548(98)00104-x
URL : http://www.uv.es/rmarti/paper/docs/lop1.pdf

G. Lebanon and J. Lafferty, Conditional models on the ranking poset, Proceedings of NIPS'03, 2003.

B. Mandhani and M. Meila, Tractable search for learning exponential models of rankings, Proceedings of AISTATS, vol.5, 2009.

M. Meila, K. Phadnis, A. Patterson, and J. Bilmes, Consensus ranking under the exponential model, Conference on Artificial Intelligence (UAI), pp.729-734, 2007.

P. W. Mielke and K. J. Berry, , 2001.

A. Nemirovski, Ecole d'ete de Probabilities de SaintFlour XXVIII -1998, chapter Topics in Non-Parametric Statistics, Lectures on Probability Theory and Statistics, 2000.

D. M. Pennock, E. Horvitz, and C. L. Giles, Social choice theory and recommender systems: analysis of the foundations of collaborative filtering, National Conference on Artificial Intelligence, pp.729-734, 2000.

J. C. Spall, Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, 2003.

Y. Wakabayashi, The complexity of computing medians of relations, Resenhas, vol.3, issue.3, pp.323-349, 1998.