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Odone, \u201cStructured multi-class feature selection with an application to face recognition,\u201d Pattern Recognition Letters, vol.55, pp.35-41, 2015. 10.1016\/j.patrec.2014.07.004","DOI":"10.1016\/j.patrec.2014.07.004"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] M. Bennamoun, Y. Guo, and F. Sohel, \u201cFeature selection for 2D and 3D face recognition,\u201d Wiley Encyclopedia of Electrical and Electronics Engineering, 2015.","DOI":"10.1002\/047134608X.W8257"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] K. Javed, S. Maruf, and H.A. Babri, \u201cA two-stage Markov blanket based feature selection algorithm for text classification,\u201d Neurocomputing, vol.157, pp.91-104, 2015. 10.1016\/j.neucom.2015.01.031","DOI":"10.1016\/j.neucom.2015.01.031"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] J.-C. Lamirel, P. Cuxac, A.S. Chivukula, and K. Hajlaoui, \u201cOptimizing text classification through efficient feature selection based on quality metric,\u201d Journal of Intelligent Information Systems, vol.45, no.3, pp.379-396, 2015. 10.1007\/s10844-014-0317-4","DOI":"10.1007\/s10844-014-0317-4"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] Y. Cong, S. Wang, J. Liu, J. Cao, Y. Yang, and J. Luo, \u201cDeep sparse feature selection for computer aided endoscopy diagnosis,\u201d Pattern Recognition, vol.48, no.3, pp.907-917, 2015. 10.1016\/j.patcog.2014.09.010","DOI":"10.1016\/j.patcog.2014.09.010"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] T.W. Rauber, F. de Assis Boldt, and F.M. Varej\u00e3o, \u201cHeterogeneous feature models and feature selection applied to bearing fault diagnosis,\u201d IEEE Trans. Ind. Electron., vol.62, no.1, pp.637-646, 2015. 10.1109\/tie.2014.2327589","DOI":"10.1109\/TIE.2014.2327589"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] Y. Zhang, D. Gong, Y. Hu, and W. Zhang, \u201cFeature selection algorithm based on bare bones particle swarm optimization,\u201d Neurocomputing, vol.148, pp.150-157, 2015. 10.1016\/j.neucom.2012.09.049","DOI":"10.1016\/j.neucom.2012.09.049"},{"key":"11","unstructured":"[11] A.W. Whitney, \u201cA direct method of nonparametric measurement selection,\u201d IEEE Trans. Comput., vol.100, no.9, pp.1100-1103, 1971."},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] T. Marill and D. Green, \u201cOn the effectiveness of receptors in recognition systems,\u201d IEEE Trans. Inf. Theory, vol.9, no.1, pp.11-17, 1963. 10.1109\/tit.1963.1057810","DOI":"10.1109\/TIT.1963.1057810"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] R. Caruana and D. Freitag, \u201cGreedy Attribute Selection,\u201d ICML, pp.28-36, 1994.","DOI":"10.1016\/B978-1-55860-335-6.50012-X"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] M.M. Kabir, M. Shahjahan, and K. Murase, \u201cA new hybrid ant colony optimization algorithm for feature selection,\u201d Expert Systems with Applications, vol.39, no.3, pp.3747-3763, 2012. 10.1016\/j.eswa.2011.09.073","DOI":"10.1016\/j.eswa.2011.09.073"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] S.M. Vieira, L.F. Mendon\u00e7a, G.J. Farinha, and J.M.C. Sousa, \u201cModified binary PSO for feature selection using SVM applied to mortality prediction of septic patients,\u201d Applied Soft Computing, vol.13, no.8, pp.3494-3504, 2013. 10.1016\/j.asoc.2013.03.021","DOI":"10.1016\/j.asoc.2013.03.021"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] R. Forsati, A. Moayedikia, R. Jensen, M. Shamsfard, and M.R.Meybodi, \u201cEnriched ant colony optimization and its application in feature selection,\u201d Neurocomputing, vol.142, pp.354-371, 2014. 10.1016\/j.neucom.2014.03.053","DOI":"10.1016\/j.neucom.2014.03.053"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] X.-S. 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