[1] Li, J., Cheng, K., Wang, S., Morstatter, F.,Trevino, R, P., Tang, J., Liu, H. (2017) Feature selection: A data perspective. ACM computing surveys (CSUR), 50(6),1–45.
[2] Narendra and Fukunaga. (1977) A branch and bound algorithm for feature subset selection. IEEE Transactions on computers, 100(9), 917–922.
[3] Dash, M and Liu, H. (2003) Consistency-based search in feature selection. Artificial intelligence, 151(1-2), 155–176.
[4] Yamada, M., Jitkrittum, W., Sigal, L., Xing, E. P., Sugiyama, M. (2014) High-dimensional feature selection by feature-wise kernelized lasso. Neural computation, 26(1), 185–207.
[5] Bahassine, S., Madani, A., Al-Sarem, M., Kissi, M. (2020) Feature selection using an improved Chi- square for Arabic text classification. Journal of King Saud University-Computer and Information Sciences, 32(2), 225–231.
[6] Zadeh, L. A. (1965) Fuzzy sets. Information and control, 8(3), 338–353.
[7] Lee, H., Chen, Ch,. Chen, J., Jou, Y. (2001) An efficient fuzzy classifier with feature selection based on fuzzy entropy, IEEE transactions on systems, man, and cybernetics, part B (cybernetics), 32(3), 426–432.
[8] Hoque, N., Ahmed, HA., Bhattacharyya, DK., Kalita, JK. (2016) A fuzzy mutual information-based feature selection method for classification, Fuzzy Information and Engineering, 8(3), 355–384.
[9] Harish, BS and Revanasiddappa, MB. (2018) A new feature selection method based on intuitionistic fuzzy entropy to categorize text documents. International Journal of Interactive Multimedia and Artificial Intelligence ….
[10] De Luca, A and Termini, S. (1993) A definition of a non-probabilistic entropy in the setting of fuzzy sets theory. Elsevier, 197–202.
[11] Khushaba, R. N., Kodagoda, S., Lal, S., Dissanayake, G. (2010) Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. IEEE transactions on biomedical engineering, 58(1), 121–131.
