A. Ben-hur, D. Horn, H. Siegelmann, and V. Vapnik, Support vector clustering, Scholarpedia, vol.3, issue.6, pp.125-137, 2001.
DOI : 10.4249/scholarpedia.5187

J. Boulicaut and J. Besson, Actionability and formal concepts: A data mining perspective. Formal Concept Analysis, pp.14-31, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01500630

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.2307/1403680

L. Cao, Actionable knowledge discovery and delivery, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol.18, issue.2, pp.149-163, 2012.
DOI : 10.1016/j.engappai.2005.06.004

L. Cao, Data Science, ACM Computing Surveys, vol.50, issue.3, p.43, 2017.
DOI : 10.1214/aoms/1177704711

URL : https://hal.archives-ouvertes.fr/in2p3-01314863

L. Cao, Data science, Communications of the ACM, vol.60, issue.8, pp.59-68, 2017.
DOI : 10.1214/aoms/1177704711

URL : https://hal.archives-ouvertes.fr/in2p3-01314863

L. Chang, S. Roberts, and A. Welsh, Robust Lasso Regression Using Tukey's Biweight Criterion, Technometrics, vol.7, p.2017
DOI : 10.1214/009053607000000127

D. Che, M. Safran, and Z. Peng, From Big Data to Big Data Mining: Challenges, Issues, and Opportunities, International Conference on Database Systems for Advanced Applications, pp.1-15, 2013.
DOI : 10.1007/978-3-642-40270-8_1

C. Philip, C. , and C. Zhang, Data-intensive applications, challenges, techniques and technologies: A survey on Big Data, Information Sciences, vol.275, pp.314-347, 2014.
DOI : 10.1016/j.ins.2014.01.015

B. Deville, Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner, 2006.

G. George, M. R. Haas, and A. Pentland, Big Data and Management, Academy of Management Journal, vol.57, issue.2, pp.321-326, 2014.
DOI : 10.5465/amj.2014.4002

C. Gini, On the measure of concentration with special reference to income and statistics

A. James, . Hanley, J. Barbara, and . Mcneil, The meaning and use of the area under a receiver operating characteristic (roc) curve, Radiology, vol.143, issue.1, pp.29-36, 1982.

K. Bertrand and . Hassani, Artificial neural network to serve scenario analysis purposes, Scenario Analysis in Risk Management, pp.111-121

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data mining ,Inference,and Prediction, 2009.

E. Timothy, K. E. Hewett, W. J. Webster, and . Hurd, Systematic selection of key logistic regression variables for risk prediction analyses: A five-factor maximum model, Clinical Journal of Sport Medicine, 2017.

A. Jacobs, The pathologies of big data, Communications of the ACM, vol.52, issue.8, pp.36-44, 2009.
DOI : 10.1145/1536616.1536632

A. Jog, A. Carass, S. Roy, L. Dzung, J. L. Pham et al., Random forest regression for magnetic resonance image synthesis, Medical Image Analysis, vol.35, pp.475-488, 2017.
DOI : 10.1016/j.media.2016.08.009

D. Lazer, R. Kennedy, G. King, and A. Vespignani, The Parable of Google Flu: Traps in Big Data Analysis, Science, vol.20, issue.3, pp.1203-1205, 2014.
DOI : 10.1093/pan/mpr057

N. Mathur and R. Purohit, Issues and Challenges in Convergence of Big Data, Cloud and Data Science, International Journal of Computer Applications, vol.160, issue.9, p.2017
DOI : 10.5120/ijca2017913082

G. Neyer, Next generation payments: Alternative models or converging paths, Journal of Payments Strategy & Systems, vol.11, issue.1, pp.34-41, 2017.

Z. Shi, P. Siva, and T. Xiang, Transfer Learning by Ranking for Weakly Supervised Object Annotation, Procedings of the British Machine Vision Conference 2012, 2017.
DOI : 10.5244/C.26.78

S. Suthaharan, Support vector machine In Machine Learning Models and Algorithms for Big Data Classification, pp.207-235

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996.

J. Zhu, H. Zou, S. Rosset, and T. Hastie, Multi-class adaboost, Statistics and its Interface, vol.2, issue.3, pp.349-360, 2009.

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.5, issue.2, pp.301-320, 2005.
DOI : 10.1073/pnas.201162998