S. Ali and K. A. Smith, On learning algorithm selection for classification, Applied Soft Computing, vol.6, issue.2, pp.119-138, 2006.

B. Auder and B. Iooss, Global sensitivity analysis based on entropy, Safety, reliability and risk analysis-Proceedings of the ESREL 2008 Conference, pp.2107-2115, 2008.

A. Backhaus and U. Seiffert, Quantitative measurements of model interpretability for the analysis of spectral data, Computational Intelligence and Data Mining (CIDM), 2013 IEEE Symposium on, pp.18-25, 2013.

R. Badii and A. Politi, Complexity: Hierarchical structures and scaling in physics, vol.6, 1999.

D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen et al., How to explain individual classification decisions, Journal of Machine Learning Research, vol.11, pp.1803-1831, 2010.

A. Ben-hur and J. Weston, A user's guide to support vector machines, Data mining techniques for the life sciences, pp.223-239, 2010.

S. Eta and . Berner, Clinical Decision Support Systems, 2007.

S. Boughorbel, J. Tarel, and N. Boujemaa, Conditionally positive definite kernels for svm based image recognition, Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on, pp.113-116, 2005.

J. M. Mikio-l-braun, K. Buhmann, and . Mã?ller, On relevant dimensions in kernel feature spaces, Journal of Machine Learning Research, vol.9, pp.1875-1908, 2008.

L. Breiman, Statistical modeling: The two cultures (with comments and a rejoinder by the author), Statistical Science, vol.16, issue.3, pp.199-231, 2001.

M. André, P. W. Carrington, H. H. Fieguth, and . Chen, A new mercer sigmoid kernel for clinical data classification, Engineering in Medicine and Biology Society (EMBC), pp.6397-6401, 2014.

R. Caruana and A. Niculescu-mizil, An empirical comparison of supervised learning algorithms, Proceedings of the 23rd international conference on Machine learning, pp.161-168, 2006.

A. Cotter, J. Keshet, and N. Srebro, Explicit approximations of the gaussian kernel, 2011.

M. Thomas, . Cover, A. Joy, and . Thomas, Elements of information theory, 2012.

A. Joseph, D. S. Cruz, and . Wishart, Applications of machine learning in cancer prediction and prognosis, Cancer informatics, issue.2, 2006.

O. Devos, C. Ruckebusch, A. Durand, L. Duponchel, and J. Huvenne, Support vector machines (svm) in near infrared (nir) spectroscopy: Focus on parameters optimization and model interpretation, vol.96, pp.27-33, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00368855

F. Doshi, -. Velez, and B. Kim, Towards a rigorous science of interpretable machine learning, 2017.

D. Freedman and P. Diaconis, On the histogram as a density estimator: L 2 theory. Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete, vol.57, pp.453-476, 1981.

M. Gell-mann and S. Lloyd, Information measures, effective complexity, and total information, Complexity, vol.2, issue.1, pp.44-52, 1996.

B. Goodman and S. Flaxman, European union regulations on algorithmic decisionmaking and a "right to explanation, 1st Workshop on Human Interpretability in Machine Learning, International Conference of Machine Learning, 2016.

L. David, J. R. Goodstein, and . Goodstein, Feynman's lost lecture: the motion of planets around the sun, vol.1, 1996.

A. Robert and . Greenes, Clinical decision support: the road ahead, 2011.

M. Kenneth, . Hanson, M. François, and . Hemez, Sensitivity Analysis of Model Output: Proceedings of the 4th International Conference on Sensitivity Analysis of Model Output, 2004.

A. Holzinger, C. Biemann, C. S. Pattichis, and D. Kell, What do we need to build explainable ai systems for the medical domain, 2017.

E. Marvin, P. Jernigan, and . Fieguth, Introduction to Pattern Recognition, 2004.

G. Maurice and . Kendall, The treatment of ties in ranking problems, Biometrika, vol.33, issue.3, pp.239-251, 1945.

V. Lemaire, R. Féraud, and N. Voisine, Contact personalization using a score understanding method, IEEE International Joint Conference on, pp.649-654, 2008.

P. Liang, Provenance and contracts in machine learning, Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning, 2016.

D. Lin, An information-theoretic definition of similarity, ICML, vol.98, pp.296-304, 1998.

. Zachary-c-lipton, C. David, C. Kale, R. Elkan, S. Wetzell et al., The mythos of model interpretability, IEEE Spectrum, 2016.

J. G. Paulo and . Lisboa, Interpretability in machine learning-principles and practice, International Workshop on Fuzzy Logic and Applications, pp.15-21, 2013.

H. Liu, W. Chen, and A. Sudjianto, Relative entropy based method for probabilistic sensitivity analysis in engineering design, Journal of Mechanical Design, vol.128, issue.2, pp.326-336, 2006.

S. Lloyd, Measures of complexity: a nonexhaustive list, IEEE Control Systems Magazine, vol.21, issue.4, pp.7-8, 2001.

Y. Lou, R. Caruana, and J. Gehrke, Intelligible models for classification and regression, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.150-158, 2012.

D. Martens and B. Baesens, Building acceptable classification models, Data Mining, pp.53-74, 2010.

J. Mcdermott, S. Richard, and . Forsyth, Diagnosing a disorder in a classification benchmark, Pattern Recognition Letters, vol.73, pp.41-43, 2016.

J. Mercer, Functions of positive and negative type, and their connection with the theory of integral equations. Philosophical transactions of the royal society of London. Series A, containing papers of a mathematical or physical character, pp.415-446, 1909.

T. Miller, P. Howe, and L. Sonenberg, Explainable ai: Beware of inmates running the asylum, IJCAI-17 Workshop on Explainable AI (XAI), p.36, 2017.

G. Montavon, S. Lapuschkin, A. Binder, W. Samek, and K. Müller, Explaining nonlinear classification decisions with deep taylor decomposition, Pattern Recognition, vol.65, pp.211-222, 2017.

J. Nahar, S. Ali, and Y. Chen, Microarray data classification using automatic svm kernel selection, DNA and cell biology, vol.26, issue.10, pp.707-712, 2007.

S. Randal, W. L. Olson, P. Cava, . Orzechowski, J. Ryan et al., Pmlb: a large benchmark suite for machine learning evaluation and comparison. BioData mining, vol.10, p.36, 2017.

P. Santoro-perez, S. R. Nozawa, A. A. Macedo, and J. Baranauskas, Windowing improvements towards more comprehensible models. KnowledgeBased Systems, vol.92, pp.9-22, 2016.

B. Poulin, R. Eisner, D. Szafron, P. Lu, R. Greiner et al., Visual explanation of evidence with additive classifiers, Proceedings Of The National Conference On Artificial Intelligence, vol.21, p.1822, 1999.

K. Martin-v-pusic, R. Boutis, D. Hatala, and . Cook, Learning curves in health professions education, Academic Medicine, vol.90, issue.8, pp.1034-1042, 2015.

A. Rényi, On measures of entropy and information, Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, vol.1, 1961.

S. Marco-tulio-ribeiro, C. Singh, and . Guestrin, Why should i trust you?: Explaining the predictions of any classifier, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1135-1144, 2016.

W. David and . Scott, On optimal and data-based histograms, Biometrika, vol.66, issue.3, pp.605-610, 1979.

S. Selvin, Statistical analysis of epidemiologic data, 2004.

J. Shawe, -. Taylor, and N. Cristianini, Kernel methods for pattern analysis, 2004.

E. Sober, Parsimony and predictive equivalence, Erkenntnis, vol.44, issue.2, pp.167-197, 1996.

M. Ilya and . Sobol, Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates, Mathematics and computers in simulation, vol.55, issue.1, pp.271-280, 2001.

S. Stevens, On the theory of scales of measurement, 1946.

A. Herbert and . Sturges, The choice of a class interval, Journal of the american statistical association, vol.21, issue.153, pp.65-66, 1926.

Z. Szabó, B. Póczos, and . Orincz, Undercomplete blind subspace deconvolution, Journal of Machine Learning Research, vol.8, pp.1063-1095, 2007.

Z. Szabó, B. Póczos, and . Orincz, Separation theorem for independent subspace analysis and its consequences, Pattern Recognition, vol.45, pp.1782-1791, 2012.

C. Tsallis, Possible generalization of boltzmann-gibbs statistics, Journal of statistical physics, vol.52, issue.1, pp.479-487, 1988.

A. Tussy and . Gustafson, Elementary Algebra. Nelson Education, 2012.