S. C. Chen, Z. Wang, and Y. J. Tian, Matrix-pattern-oriented Ho???Kashyap classifier with regularization learning, Pattern Recognition, vol.40, issue.5, pp.533-1543, 2007.
DOI : 10.1016/j.patcog.2006.09.001

Z. Wang and S. C. Chen, New Least Squares Support Vector Machines Based on Matrix Patterns, Neural Processing Letters, vol.16, issue.5???6, pp.41-56, 2007.
DOI : 10.1017/CBO9780511801389

URL : http://parnec.nuaa.edu.cn/papers/journal/2007/zwang-npl-07.pdf

Y. Yan, Q. Wang, G. Ni, Z. Pan, and R. Kong, One-Class Support Vector Machines Based on Matrix Patterns, International Conference on Informatics, Cybernetics, and Computer Engineering, pp.223-231, 2012.
DOI : 10.1007/978-3-642-25188-7_27

Y. B. Xie, Z. H. Zhang, and W. J. Li, The 32nd International Conference on Machine Learning, 2015.

H. Dai, Class imbalance learning via a fuzzy total margin based support vector machine, Applied Soft Computing, vol.31, pp.172-184, 2015.
DOI : 10.1016/j.asoc.2015.02.025

X. Deng and X. Tian, Nonlinear process fault pattern recognition using statistics kernel PCA similarity factor, Neurocomputing, vol.121, pp.298-308, 2013.
DOI : 10.1016/j.neucom.2013.04.042

Y. Guo and H. Z. Zhang, Oil spill detection using synthetic aperture radar images and feature selection in shape space, International Journal of Applied Earth Observation and Geoinformation, vol.30, pp.146-157, 2014.
DOI : 10.1016/j.jag.2014.01.011

A. Ozcift and A. Gulten, Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms, Computer Methods and Programs in Biomedicine, vol.104, issue.3, pp.443-451, 2011.
DOI : 10.1016/j.cmpb.2011.03.018

I. Brown and C. Mues, An experimental comparison of classification algorithms for imbalanced credit scoring data sets, Expert Systems with Applications, vol.39, issue.3, pp.3446-3453, 2012.
DOI : 10.1016/j.eswa.2011.09.033

C. Lin and S. Wang, Fuzzy Support Vector Machines, IEEE Transactions on Neural Networks, vol.13, issue.2, pp.464-471, 2002.

Y. Wang, S. Wang, and K. K. Lai, A new fuzzy support vector machine to evaluate credit risk, IEEE Transactions on Fuzzy Systems, vol.13, issue.6, pp.820-831, 2005.
DOI : 10.1109/TFUZZ.2005.859320

C. E. Shannon, A mathematical theory of communication, ACM SIGMOBILE Mobile Computing and Communications Review, vol.5, issue.1, pp.3-55, 2001.
DOI : 10.1145/584091.584093

J. Alcala-fdez, A. Fernandez, J. Luengo, J. Derrac, S. Garcia et al., A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems Journal of Multiple-Valued Logic and Soft Computing, pp.2-3, 2011.

J. Alcala-fdez, L. Sanchez, S. Garcia, M. J. Jesus, S. Ventura et al., A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems. Soft Computing, pp.307-318, 2009.

S. A. Nene, S. K. Nayar, and H. Murase, Columbia Object Image Library (COIL-20), 1996.

B. B. Cun, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard et al., Handwritten digit recognition with a back-propagation network Advances in neural information processing systems, 1990.

F. Bennett, T. Richardson, and A. Harter, Teleporting-making applications mobile. Mobile Computing Systems and Applications, pp.82-84, 1994.

N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar, Attribute and simile classifiers for face verification, 2009 IEEE 12th International Conference on Computer Vision, pp.365-372, 2009.
DOI : 10.1109/ICCV.2009.5459250

B. A. Smith, Q. Yin, S. K. Feiner, and S. K. Nayar, Gaze Locking, Passive Eye Contact Detection for Human-Object Interaction, ACM Symposium on User Interface Software and Technology (UIST), pp.271-280, 2013.

J. Milgram, M. Cheriet, and R. Sabourin, One Against One " or " One Against All " , Which One is Better for Handwriting Recognition with SVMs, Tenth International Workshop on Frontiers in Handwriting Recognition, 2013.
URL : https://hal.archives-ouvertes.fr/inria-00103955

R. Debnath, N. Takahide, and H. Takahashi, A decision based one-against-one method for multi-class support vector machine. Pattern analysis and applications, pp.164-175, 2004.
DOI : 10.1007/s10044-004-0213-6

C. W. Hsu and C. J. Lin, A comparison of methods for multiclass support vector machines, IEEE Transactions on Neural Networks, vol.13, issue.2, pp.415-425, 2002.

C. Cortes and V. Vapnik, Machine Learning, vol.20, issue.3, pp.273-297, 1995.

J. Huang and C. X. Ling, Using AUC and accuracy in evaluating learning algorithms, IEEE Transactions on Knowledge and Data Engineering, vol.17, issue.3, pp.299-310, 2005.
DOI : 10.1109/TKDE.2005.50

U. M. Braga-neto and E. R. Dougherty, Is cross-validation valid for small-sample microarray classification?, Bioinformatics, vol.20, issue.3, pp.374-380, 2004.
DOI : 10.1093/bioinformatics/btg419

J. Demsar, Statistical comparisons of classifiers over multiple data sets, The Journal of Machine Learning Research, vol.7, pp.1-30, 2006.

C. M. Zhu and Z. Wang, Entropy-based matrix learning machine for imbalanced data sets, Pattern Recognition Letters, vol.88, issue.1, pp.72-80, 2017.
DOI : 10.1016/j.patrec.2017.01.014