A. E. Akadi, A. E. Ouardighi, and D. Aboutajdine, A powerful feature selection approach based on mutual information, International Journal of Computer Science and Network Security, pp.116-121, 2008.

S. Alelyani, On Feature Selection Stability: A Data Perspective

R. Battiti, Using mutual information for selecting features in supervised neural net learning, IEEE Transactions on Neural Networks, vol.5, issue.4, pp.537-550, 1994.
DOI : 10.1109/72.298224

A. L. Blum and P. Langley, Selection of relevant features and examples in machine learning, Artificial Intelligence, vol.97, issue.1-2, pp.1-2, 1997.
DOI : 10.1016/S0004-3702(97)00063-5

G. Brown, A. Pocock, M. Zhao, and M. Luján, Conditional likelihood maximisation: A unifying framework for information theoretic feature selection, J. Mach. Learn. Res, vol.13, issue.1, pp.27-66, 2012.

S. Das, Filters, wrappers and a boosting-based hybrid for feature selection, Proceedings of the Eighteenth International Conference on Machine Learning ICML '01, pp.74-81, 2001.

M. Dash and H. Liu, Feature selection for classification, Intelligent Data Analysis, vol.1, issue.1-4, pp.131-156, 1997.
DOI : 10.1016/S1088-467X(97)00008-5

J. Deng, A. Berg, and L. Fei-fei, Hierarchical semantic indexing for large scale image retrieval, CVPR 2011, pp.785-792, 2011.
DOI : 10.1109/CVPR.2011.5995516

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2000.

F. Fleuret, Fast binary feature selection with conditional mutual information, J. Mach. Learn. Res, vol.5, pp.1531-1555, 2004.

G. Forman, An extensive empirical study of feature selection metrics for text classification, J. Mach. Learn. Res, vol.3, pp.1289-1305, 2003.

G. Golub, V. Loan, and C. , Matrix Computations. Matrix Computations, 2012.

Q. Gu, Z. Li, and J. Han, Generalized fisher score for feature selection, p.3725, 2012.

Z. Gu, R. Eils, and M. Schlesner, Complex heatmaps reveal patterns and correlations in multidimensional genomic data, Bioinformatics, vol.32, issue.18, pp.2847-2849, 2016.
DOI : 10.1016/j.ccr.2012.08.024

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, J. Mach. Learn. Res, vol.3, pp.1157-1182, 2003.

T. J. Hastie, R. J. Tibshirani, and J. H. Friedman, The elements of statistical learning : data mining, inference, and prediction. Springer series in statistics, Autres impressions, pp.2011-2013, 2009.

C. Huertas and R. Juárez-ramírez, Heat Map Based Feature Selection: A Case Study for Ovarian Cancer, Applications of Evolutionary Computation -18th European Conference Proceedings (2015), pp.3-13, 2015.
DOI : 10.1007/978-3-319-16549-3_1

G. Hughes, On the mean accuracy of statistical pattern recognizers, IEEE Transactions on Information Theory, vol.14, issue.1, pp.55-63, 1968.
DOI : 10.1109/TIT.1968.1054102

A. Jain and B. Chandrasekaran, Dimensionality and sample size considerations, In Pattern Recognition in, pp.835-855, 1982.

G. H. John, R. Kohavi, and K. Pfleger, Irrelevant Features and the Subset Selection Problem, In MACHINE LEARNING: PROCEEDINGS OF THE ELEVENTH INTERNATIONAL, pp.121-129, 1994.
DOI : 10.1016/B978-1-55860-335-6.50023-4

R. Kohavi, J. , and G. , Wrappers for feature subset selection, Artificial Intelligence, vol.97, issue.1-2, pp.273-324, 1997.
DOI : 10.1016/S0004-3702(97)00043-X

J. Li, K. Cheng, S. Wang, F. Morstatter, R. P. Trevino et al., Feature Selection, ACM Computing Surveys, vol.50, issue.6, p.7996, 2016.
DOI : 10.1109/ICDM.2017.78
URL : https://hal.archives-ouvertes.fr/hal-01557791

D. Lin and X. Tang, Conditional Infomax Learning: An Integrated Framework for Feature Extraction and Fusion, pp.68-82, 2006.
DOI : 10.1109/34.879790

F. T. Liu, K. M. Ting, and Z. Zhou, Isolation Forest, 2008 Eighth IEEE International Conference on Data Mining, pp.413-422, 2008.
DOI : 10.1109/ICDM.2008.17

P. E. Meyer and G. Bontempi, On the Use of Variable Complementarity for Feature Selection in Cancer Classification, EvoWorkshops, pp.91-102, 2006.
DOI : 10.1007/11732242_9

P. Mitra, C. A. Murthy, and S. K. Pal, Unsupervised feature selection using feature similarity, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.3, pp.301-312, 2002.
DOI : 10.1109/34.990133

A. Navot, R. Gilad-bachrach, Y. Navot, and N. Tishby, Is feature selection still necessary? In SLSFS, 2005.

J. Gunn and . Shawe-taylor, Lecture Notes in Computer Science, vol.3940, pp.127-138

A. Y. Ng, On feature selection: Learning with exponentially many irrelevant features as training examples, Proceedings of the Fifteenth International Conference on Machine Learning, pp.404-412, 1998.

H. Peng, F. Long, and C. Ding, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.8, pp.1226-1238, 2005.
DOI : 10.1109/TPAMI.2005.159

A. Pujol, C. , and L. , Color quantization for image processing using self information, 2007 6th International Conference on Information, Communications & Signal Processing, pp.1-5, 2007.
DOI : 10.1109/ICICS.2007.4449822
URL : https://hal.archives-ouvertes.fr/hal-01502228

R. Sikonja, M. Kononenko, and I. , Theoretical and empirical analysis of relieff and rrelieff, Mach. Learn, vol.53, pp.1-2, 2003.

Y. Rui and T. S. Huang, Image Retrieval: Current Techniques, Promising Directions, and Open Issues, Journal of Visual Communication and Image Representation, vol.10, issue.1, pp.39-62, 1999.
DOI : 10.1006/jvci.1999.0413

Y. Saeys, I. N. Inza, and P. Larrañaga, A review of feature selection techniques in bioinformatics, Bioinformatics, vol.7, issue.5, pp.2507-2517, 2007.
DOI : 10.1186/1471-2105-7-197

M. Tan, I. W. Tsang, W. , and L. , Towards ultrahigh dimensional feature selection for big data, Journal of Machine Learning Research, vol.15, pp.1371-1429, 2014.

X. Wang and O. Gotoh, Accurate molecular classification of cancer using simple rules, BMC Medical Genomics, vol.5, issue.2, pp.1-23, 2009.
DOI : 10.1145/980972.980978

P. Wittek, Somoclu: An efficient distributed library for self-organizing maps, p.1422, 2013.

S. Xie, Principal Component Analysis Based Feature Extraction Methods Applied to Biomedical and Communication Network Data, pp.2010-67857

J. Zhou, J. Liu, V. A. Narayan, Y. , and J. , Modeling disease progression via fused sparse group lasso, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pp.1095-1103
DOI : 10.1145/2339530.2339702