W. Yang, D. Li, L. Zhu, Y. Kang, and F. Li, A new approach for image processing in foreign fiber detection, Computers and Electronics in Agriculture, vol.68, issue.1, pp.68-77, 2009.
DOI : 10.1016/j.compag.2009.04.005

D. Li, W. Yang, and S. Wang, Classification of foreign fibers in cotton lint using machine vision and multi-class support vector machine, Computers and Electronics in Agriculture, vol.74, issue.2, pp.274-279, 2010.
DOI : 10.1016/j.compag.2010.09.002

W. Yang, S. Lu, S. Wang, and D. Li, Fast recognition of foreign fibers in cotton lint using machine vision, Mathematical and Computer Modelling, vol.54, issue.3-4, pp.877-882, 2011.
DOI : 10.1016/j.mcm.2010.11.010

J. Y. Lin, H. R. Ke, B. C. Chien, and W. P. Yang, Classifier design with feature selection and feature extraction using layered genetic programming, Expert Systems with Applications, vol.34, issue.2, pp.1384-1393, 2008.
DOI : 10.1016/j.eswa.2007.01.006

V. Bolón-canedo, N. Sá-nchez-maro-?-o, and A. Alonso-betanzos, A review of feature selection methods on synthetic data, Knowledge and Information Systems, vol.97, issue.1, pp.483-519, 2013.
DOI : 10.1073/pnas.97.1.262

Z. Sun, G. Bebis, and R. Miller, Object detection using feature subset selection, Pattern Recognition, vol.37, issue.11, pp.2165-2176, 2004.
DOI : 10.1016/j.patcog.2004.03.013
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.1942

M. Pedergnana, P. R. Marpu, D. Mura, M. Benediktsson, J. A. Bruzzone et al., A Novel Technique for Optimal Feature Selection in Attribute Profiles Based on Genetic Algorithms, IEEE Transactions on Geoscience and Remote Sensing, vol.51, issue.6, pp.3514-3528, 2013.
DOI : 10.1109/TGRS.2012.2224874
URL : https://hal.archives-ouvertes.fr/hal-00944228

H. L. Chen, B. Yang, G. Wang, J. Liu, X. Xu et al., A Novel Bankruptcy Prediction Model based on an Adaptive Fuzzy K-Nearest Neighbor Method. Knowl-Based Syst, pp.24-1348, 2011.
DOI : 10.1016/j.knosys.2011.06.008

T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE Transactions on Information Theory, vol.13, issue.1, pp.21-27, 1967.
DOI : 10.1109/TIT.1967.1053964
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.2616

Z. Xuegong, Introduction to Statistical Learning Theory and Support Vector Machines, Acta Automatica Sinica, vol.26, issue.1, pp.32-42, 2000.

R. C. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp.39-43, 1995.
DOI : 10.1109/MHS.1995.494215

H. L. Chen, B. Yang, G. Wang, S. J. Wang, J. Liu et al., Support Vector Machine Based Diagnostic System for Breast Cancer Using Swarm Intelligence, Journal of Medical Systems, vol.74, issue.7???8, pp.2505-2519, 2012.
DOI : 10.1016/j.ijmedinf.2005.05.002

J. Kennedy and R. C. Eberhart, A discrete binary version of the particle swarm algorithm, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, pp.4104-4108, 1997.
DOI : 10.1109/ICSMC.1997.637339

M. Dorigo, V. Maniezzo, and A. Colorni, Ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.26, issue.1, pp.29-41, 1996.
DOI : 10.1109/3477.484436

X. Zhao, D. Li, B. Yang, C. Ma, Y. Zhu et al., Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton, Applied Soft Computing, vol.24, pp.585-596, 2014.
DOI : 10.1016/j.asoc.2014.07.024

R. Forsati, A. Moayedikia, R. Jensen, M. Shamsfard, and M. R. Meybodi, Enriched ant colony optimization and its application in feature selection, Neurocomputing, vol.142, pp.354-371, 2014.
DOI : 10.1016/j.neucom.2014.03.053
URL : http://cadair.aber.ac.uk/dspace/bitstream/2160/14265/1/Enriched_ant_colony_optimization.pdf