K. Amouzgar, S. Bandaru, and A. H. Ng, Radial basis functions with a priori bias as surrogate models: A comparative study, Engineering Applications of Artificial Intelligence, vol.71, pp.28-44, 2018.

G. Biau and E. Scornet, A random forest guided tour, Test, vol.25, issue.2, pp.197-227, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01221748

L. Breiman, Random forests, Machine learning, vol.45, issue.1, pp.5-32, 2001.

K. M. Carley, N. Y. Kamneva, and J. Reminga, Response surface methodology, DTIC Document, 2004.

L. Chen, H. Wang, F. Ye, and W. Hu, Comparative study of hdmrs and other popular metamodeling techniques for high dimensional problems. Structural and Multidisciplinary Optimization, vol.59, pp.21-42, 2019.

S. K. Dasari, N. Lavesson, P. Andersson, and M. Persson, Tree-based response surface analysis, Machine Learning, Optimization, and Big Data, pp.118-129, 2015.

H. Deng, Interpreting tree ensembles with intrees, 2014.

R. Díaz-uriarte and S. A. De-andres, Gene selection and classification of microarray data using random forest, BMC bioinformatics, vol.7, issue.1, p.1, 2006.

D. Gorissen, I. Couckuyt, P. Demeester, T. Dhaene, and K. Crombecq, A surrogate modeling and adaptive sampling toolbox for computer based design, The Journal of Machine Learning Research, vol.11, pp.2051-2055, 2010.

R. Jin, W. Chen, and T. W. Simpson, Comparative studies of metamodelling techniques under multiple modelling criteria. Structural and Multidisciplinary Optimization, vol.23, pp.1-13, 2001.

E. Kang, E. Jackson, and W. Schulte, An approach for effective design space exploration, Foundations of Computer Software. Modeling, Development, and Verification of Adaptive Systems, pp.33-54, 2010.

R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, In: Ijcai, vol.14, pp.1137-1145, 1995.

A. Liaw and M. Wiener, Classification and regression by randomforest, R news, vol.2, issue.3, pp.18-22, 2002.

Y. Mack, T. Goel, W. Shyy, and R. Haftka, Surrogate model-based optimization framework: a case study in aerospace design, Evolutionary computation in dynamic and uncertain environments, pp.323-342, 2007.

B. H. Menze, B. M. Kelm, D. N. Splitthoff, U. Koethe, and F. A. Hamprecht, On oblique random forests, Machine Learning and Knowledge Discovery in Databases, pp.453-469, 2011.

T. M. Oshiro, P. S. Perez, and J. A. Baranauskas, How many trees in a random forest? In: Machine Learning and Data Mining in Pattern Recognition, pp.154-168, 2012.

M. Preuss, T. Wagner, and D. Ginsbourger, High-dimensional model-based optimization based on noisy evaluations of computer games, pp.145-159, 2012.

N. V. Queipo, R. T. Haftka, W. Shyy, T. Goel, R. Vaidyanathan et al., Surrogate-based analysis and optimization, Progress in aerospace sciences, vol.41, issue.1, pp.1-28, 2005.

H. Sathyanarayanamurthy and R. B. Chinnam, Metamodels for variable importance decomposition with applications to probabilistic engineering design, Computers & Industrial Engineering, vol.57, issue.3, pp.996-1007, 2009.

S. Shan and G. G. Wang, Survey of modeling and optimization strategies to solve highdimensional design problems with computationally-expensive black-box functions, Structural and Multidisciplinary Optimization, vol.41, issue.2, pp.219-241, 2010.

D. J. Sheskin, Handbook of parametric and nonparametric statistical procedures, 2003.

T. W. Simpson, T. M. Mauery, J. J. Korte, and F. Mistree, Comparison of response surface and kriging models in the multidisciplinary design of an aerospike nozzle, 1998.

G. G. Wang and S. Shan, Review of metamodeling techniques in support of engineering design optimization, Journal of Mechanical design, vol.129, issue.4, pp.370-380, 2007.

I. H. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques, 2011.

D. Zhao and D. Xue, A comparative study of metamodeling methods considering sample quality merits. Structural and Multidisciplinary Optimization, vol.42, pp.923-938, 2010.