M. Shouman and T. Turner, Using decision tree for diagnosing heart disease patients, Proc. of 9th Australasian Data Mining Conference, pp.23-30, 2011.

W. Alghamdi and D. Stamate, A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp.825-830, 2016.
DOI : 10.1109/ICMLA.2016.0148

URL : https://kclpure.kcl.ac.uk/portal/files/73654164/Paper_A_PredictionModelling_and_PatternDetectionApproach_for_the_FirstEpisodePsychosisAssociated_to_CannabisUse.pdf

J. Han, M. Kamber, and J. Pei, Data Mining Concepts and Techniques, pp.279-328, 2011.

I. Witten and E. Frank, Data mining, ACM SIGMOD Record, vol.31, issue.1, 2016.
DOI : 10.1145/507338.507355

J. Quinlan, Induction of decision trees, Machine Learning, pp.81-106, 1986.
DOI : 10.1037/13135-000

URL : https://link.springer.com/content/pdf/10.1007%2FBF00116251.pdf

P. Tan, S. Michael, and K. Vipin, Introduction to Data Mining, 2005.

L. Breiman and J. Friedman, Classification and regression trees, Machine Learning, 1984.

W. Buntine and T. Niblett, A further comparison of splitting rules for decision-tree induction, Machine Learning, pp.75-85, 1992.
DOI : 10.1111/j.1469-1809.1936.tb02137.x

W. Liu and A. White, The importance of attribute selection measures in decision tree induction, Machine Learning, 1994.
DOI : 10.1007/BF01000407

M. Ojala and G. Garriga, Permutation Tests for Studying Classifier Performance, 2009 Ninth IEEE International Conference on Data Mining, pp.1833-1863, 2010.
DOI : 10.1109/ICDM.2009.108

P. Good, Permutation tests: a practical guide to resampling methods for testing hypotheses; Springer series in statistics, 2000.

T. Maszczyk and W. Duch, Comparison of Shannon, Renyi and Tsallis Entropy Used in Decision Trees, Artificial Intelligence and Soft Computing?ICAISC, pp.643-651, 2008.
DOI : 10.1007/978-3-540-69731-2_62

L. Raileanu and K. Stoffel, Theoretical Comparison between the Gini Index and Information Gain Criteria, Annals of Mathematics and Artificial Intelligence, vol.41, issue.1, p.7793, 2004.
DOI : 10.1023/B:AMAI.0000018580.96245.c6

C. Tsallis and R. Mendes, The role of constraints within generalised non-extensive statistics, pp.534-554, 1998.

D. Jaroszewicz and S. Szymon, A Generalization of Conditional Entropy, 2018.

M. Kuhn and K. Johnson, Applied Predictive Modelling, 2013.

E. Frank, M. Hall, and I. Witten, The WEKA Workbench, Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, 2016.

, UCI machine learning repository: Datasets, https, pp.2017-2018

I. Guyon, J. Li, and T. Mader, Competitive baseline methods set new standards for the NIPS 2003 feature selection benchmark, Pattern Recognition Letters, vol.28, issue.12, pp.1438-1444, 2007.
DOI : 10.1016/j.patrec.2007.02.014

I. Guyon, Design of experiments of the nips 2003 variable selection benchmark, 2003.

I. Guyon and S. Gunn, Result analysis of the nips 2003 feature selection challenge, in Advances in neural information processing systems, pp.545-552, 2005.

H. Osman, Correlation-based feature ranking for online classification, in Systems, Man and Cybernetics, pp.3077-3082, 2009.
DOI : 10.1109/icsmc.2009.5346141

I. Guyon, Elisseeff, A: An introduction to feature extraction, Feature extraction, pp.1-25, 2006.