[1] M. Abdel-Basset, D. El-Shahat, I. El-Henawy, V.H.C. De Albuquerque, and S. Mirjalili, A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection, Expert Syst. Appl. 139 (2020), 112824.
[2] M.H. Aghdam, N. Ghasem-Aghaee, and M.E. Basiri, Text feature selection using ant colony optimization, Expert Syst. Appl. 36 (2009), no. 3, 6843–6853.
[3] U. Alon, N. Barkai, D.A. Notterman, K. Gish, S. Ybarra, D. Mack, and A.J. Levine, Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays, Proc. Nat. Acad. Sci. USA 96 (1999), 6745–6750.
[4] F. Amini and G. Hu, A two-layer feature selection method using Genetic Algorithm and Elastic Net, Expert Syst. Appl. 166 (2021), 114072.
[5] A. Asuncion and D. Newman, UCI repository of machine learning datasets, Available from:
!http://archive.ics.uci.edu/ml/datasets.php., 2007.
[6] S.R. Bandela and T.K. Kumar, Unsupervised feature selection and NMF de-noising for robust Speech Emotion Recognition, Appl. Acoustics 172 (2021), 107645.
[7] S. Bandyopadhyay, T. Bhadra, P. Mitra, and U. Maulik, Integration of dense subgraph finding with feature clustering for unsupervised feature selection, Pattern Recog. Lett. 40 (2014), 104–112.
[8] V.D. Blondel, J.L. Guillaume, R. Lambiotte, and E. Lefebvre, Fast unfolding of communities in large networks, J. Statist. Mech.: Theory Experiment 10008 (2008), 1–12.
[9] J.M. Cadenas, M.C. Garrido, and R. Mart´ınez, Feature subset selection Filter-Wrapper based on low quality data, Expert Syst. Appl. 40 (2013), no. 16, 6241–6252.
[10] L. Carmen, M. Reinders, and L. Wessels, Random subspace method for multivariate feature selection, Pattern Recog. Lett. 27 (2006), no. 10, 067–1076.
[11] G. Chandrashekar and F. Sahin, A survey on feature selection methods, Comput. Electric. Engin. 40 (2014), no. 1, 16–28.
[12] A.K. Farahat, A. Ghodsi, and M.S. Kamel, Efficient greedy feature selection for unsupervised learning, Knowledge Inf. Syst. 35 (2013), no. 2, 285–310.
[13] I. Guyon and A.E. Elisseeff, An introduction to variable and feature selection, J. Machine Learn. Res. 3 (2003), 1157–1182.
[14] I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines, Machine Learn. 46 (2002), no. 1, 389–422.
[15] E. Hancer, A new multi-objective differential evolution approach for simultaneous clustering and feature selection, Engin. Appl. Artific. Intell. 87 (2020), 103307.
[16] S.M. Hazrati Fard, A. Hamzeh, and S. Hashemi, Using reinforcement learning to find an optimal set of features, Comput. Math. Appl. 66 (2013), no. 10, 1892–1904.
[17] H. Liu and L. Yu, Toward integrating feature selection algorithms for classification and clustering, IEEE Trans. Knowledge Data Engin. 17 (2005), no. 4, 491–502.
[18] Y. Liu and Y.F. Zheng, FS-SFS: A novel feature selection method for support vector machines, Pattern Recog. 39 (2006), no. 7, 1333–1345.
[19] J. Kennedy and R. Eberhart, Particle swarm optimization, Proc. ICNN’95-Int. Conf. Neural Networks, IEEE, 1995, pp. 1942–1948.
[20] J. Kim, F.J. Kohout, N.H. Nie, C.H. Hull, J.G. Jenkins, K. Steinbrenner, and D.H. Bent, Statistical Package for the Social Sciences, McGraw Hill, New York NY, 1975.
[21] N. Maleki, Y. Zeinali, and S.T.A. Niaki, A k-NN method for lung cancer prognosis with the use of a genetic algorithm for feature selection, Expert Syst. Appl. 164 (2021), 113981.
[22] P. Nimbalkar and D. Kshirsagar, Feature selection for intrusion detection system in Internet-of-Things (IoT), ICT Express 7 (2021), no. 2, 177–181.
[23] M. Paniri, M.B. Dowlatshahi, and H. Nezamabadi-Pour, MLACO: A multi-label feature selection algorithm based on ant colony optimization, Knowledge-Based Syst. 192 (2020), 105285.
[24] R. Pascual-Marqui, D. Lehmann, K. Kochi, T. Kinoshita, and N. Yamada, A measure of association between vectors based on “similarity covariance”, 2013-01-21, arXiv: 1301.4291 [stat.ME]. http://arxiv.org/abs/1301.4291.
[25] G. Quanquan, L. Zhenhui, and J. Han, Generalized Fisher score for feature selection, Proc. Int. Conf. Uncertainty Artificial Intell., 2011.
[26] L.E. Raileanu and K. Stoffel, Theoretical comparison between the Gini index and information gain criteria, Ann. Math. Artif. Intell. 41 (2004), 77–93.
[27] M. Rostami, K. Berahmand, and S. Forouzandeh, A novel community detection based genetic algorithm for feature selection, J. Big Data 8 (2021), no. 1, 1–27.
[28] M. Rostami, K. Berahmand, E. Nasiri, and S. Forouzandeh, Review of swarm intelligence-based feature selection methods, Engin. Appl. Artific. Intell. 100 (2021), 104210.
[29] R. Ruiz, J.C. Riquelme, J.S. Aguilar-Ruiz, and M. Garc´ıa-Torres, Fast feature selection aimed at high-dimensional data via hybrid-sequential-ranked searches, Expert Syst. Appl. 39 (2012), 11094–11102.
[30] Y. Saeys, I. Inza, and P. Larranaga, A review of feature selection techniques in bioinformatics, Bioinformatics 23 (2007), no. 19, 2507–2517.
[31] M. Sharif, J. Amin, M. Raza, M. Yasmin, and S.C. Satapathy, An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor, Pattern Recog. Lett. 129 (2020), 150–157.
[32] C. Shi, Z. Gu, C. Duan, and Q. Tian, Multi-view adaptive semi-supervised feature selection with the self-paced learning, Signal Process. 168 (2020), 107332.
[33] Q. Song, J. Ni, and G. Wang, A fast clustering-based feature subset selection algorithm for high-dimensional data, IEEE Trans. Knowledge Data Engin. 25 (2013), no. 1, 1–14.
[34] X. Sun, Y. Liu, J. Li, J. Zhu, H. Chen, and X. Liu, Feature evaluation and selection with cooperative game theory, Pattern Recog. 45 (2012), no. 8, 2992–3002.
[35] S. Theodoridis and K. Koutroumbas, Pattern Recognition, Academic Press, Oxford, 2008.
[36] S. Theodoridis and C. Koutroumbas, Pattern Recognition, 4th Edn, Elsevier Inc, 2009.
[37] D. Wang, Z. Zhang, R. Bai, and Y. Mao, A hybrid system with filter approach and multiple population genetic algorithm for feature selection in credit scoring, J. Comput. Appl. Math. 329 (2018), 307–321.
[38] H. Xiaofei, C. Deng, and P. Niyogi, Laplacian Score for Feature Selection, Adv. Neural Inf. Process. Syst. 18 (2005), 507–514.
[39] Y. Yang, Z. Ma, A.G. Hauptmann, and N. Sebe, Feature selection for multimedia analysis by sharing information among multiple tasks, Multimedia IEEE Trans. 15 (2012), no. 3, 661–669.
[40] S. Yildirim, Y. Kaya, and F. Kılıc, A modified feature selection method based on metaheuristic algorithms for speech emotion recognition, Appl. Acoustics 173 (2021), 107721.
[41] Y. Zhang, D. Gong, X. Gao, T. Tian, and X. Sun, Binary differential evolution with self-learning for multi-objective feature selection, Inf. Sci. 507 (2020), 67–85.
[42] Z. Zhang, and E.R. Hancock, Hypergraph based information-theoretic feature selection, Pattern Recog. Lett. 33 (2012), no. 15, 1991–1999.
[43] Y. Zhou, W. Zhang, J. Kang, X. Zhang, and X. Wang, A problem-specific non-dominated sorting genetic algorithm for supervised feature selection, Inf. Sci. 547 (2021), 841–859.