References
- Abdolrazzagh-Nezhad, M., and S. Izadpanah. 2016. A modified electromagnetic-like mechanism for rough set attribute reduction. Paper read at Information and Software Technologies, Cham.
- Abdullah, S., and N. S. Jaddi. 2010. Great deluge algorithm for rough set attribute reduction. Paper read at Database Theory and Application, Bio-Science and Bio-Technology.
- Abusamra, H. 2013. A comparative study of feature selection and classification methods for gene expression data of glioma. Procedia Computer Science 23:5–14. https://doi.org/https://doi.org/10.1016/j.procs.2013.10.003.
- Acan, A., and Ü. Ahmet. 2020. Multiobjective great deluge algorithm with two-stage archive support. Engineering Applications of Artificial Intelligence 87:103239. https://doi.org/https://doi.org/10.1016/j.engappai.2019.103239.
- Aljarah, I., M. Mafarja, A. A. Heidari, H. Faris, Y. Zhang, and S. Mirjalili. 2018. Asynchronous accelerating multi-leader salp chains for feature selection. Applied Soft Computing 71:964–79. https://doi.org/https://doi.org/10.1016/j.asoc.2018.07.040.
- Anter, A. M., and M. Ali. 2020. Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems. Soft Computing 24 (3):1565–84. doi:https://doi.org/10.1007/s00500-019-03988-3.
- Arora, S., and P. Anand. 2019. Binary butterfly optimization approaches for feature selection. Expert Systems with Applications 116:147–60. doi:https://doi.org/10.1016/j.eswa.2018.08.051.
- Bai, L., Z. Han, J. Ren, and X. Qin. 2020. Research on feature selection for rotating machinery based on Supervision Kernel Entropy Component Analysis with Whale Optimization Algorithm. Applied Soft Computing 92:106245. doi:https://doi.org/10.1016/j.asoc.2020.106245.
- Baykasoglu, A. 2012. Design optimization with chaos embedded great deluge algorithm. Applied Soft Computing 12 (3):1055–67. doi:https://doi.org/10.1016/j.asoc.2011.11.018.
- Baykasoglu, A., Z. D. U. Durmusoglu, and V. Kaplanoglu. 2011. Training fuzzy cognitive maps via extended great deluge algorithm with applications. Computers in Industry 62 (2):187–95. doi:https://doi.org/10.1016/j.compind.2010.10.011.
- Blake, C. L., and C. J. Merz. UCI repository of machine learning databases 1998. Available from http://www.ics.uci.edu/~mlearn/.
- Chen, Y., D. Miao, and R. Wang. 2010. A rough set approach to feature selection based on ant colony optimization. Pattern Recognition Letters 31 (3):226–33. doi:https://doi.org/10.1016/j.patrec.2009.10.013.
- Derrac, J., C. Cornelis, S. García, and F. Herrera. 2012. Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection. Information Sciences 186 (1):73–92. doi:https://doi.org/10.1016/j.ins.2011.09.027.
- Dueck, G. 1993. New optimization heuristics: The great deluge algorithm and the record-to-record travel. Journal of Computational Physics 104 (1):86–92. doi:https://doi.org/10.1006/jcph.1993.1010.
- Emary, E., H. M. Zawbaa, and A. E. Hassanien. 2016. Binary ant lion approaches for feature selection. Neurocomputing 213:54–65. doi:https://doi.org/10.1016/j.neucom.2016.03.101.
- Han, J., M. Kamber, and J. Pei. 2011. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc, Waltham, MA, USA.
- Jaddi, N. S., and S. Abdullah. 2013a. Hybrid of genetic algorithm and great deluge for rough set attribute reduction. Turkish Journal of Electerical Engineering and Computer Sciences 21 (6):1737–50. doi:https://doi.org/10.3906/elk-1202-113.
- Jaddi, N. S., and S. Abdullah. 2013b. An Interactive Rough Set Attribute Reduction Using Great Deluge Algorithm. Paper read at Advances in Visual Informatics. Springer, Cham, New York City. https://doi.org/https://doi.org/10.1007/978-3-319-02958-0_27.
- Jaddi, N. S., and S. Abdullah. 2013c. Nonlinear great deluge algorithm for rough set attribute reduction. Journal of Information Science and Engineering 29:49–62.
- Kabir, M. M., M. Shahjahan, and K. Murase. 2012. A new hybrid ant colony optimization algorithm for feature selection. Expert Systems with Applications 39 (3):3747–63. doi:https://doi.org/10.1016/j.eswa.2011.09.073.
- Lai, C., J. T. R. Marcel, and L. Wessels. 2006. Random subspace method for multivariate feature selection. Pattern Recognition Letters 27 (10):1067–76. doi:https://doi.org/10.1016/j.patrec.2005.12.018.
- Liang, J., F. Wang, C. Dang, and Y. Qian. 2014. A group incremental approach to feature selection applying rough set technique. IEEE Transactions on Knowledge and Data Engineering 26 (2):294–308. doi:https://doi.org/10.1109/tkde.2012.146.
- Lin, S.-W., Z.-J. Lee, S.-C. Chen, and T.-Y. Tseng. 2008. Parameter determination of support vector machine and feature selection using simulated annealing approach. Applied Soft Computing 8 (4):1505–12. doi:https://doi.org/10.1016/j.asoc.2007.10.012.
- Mafarja, M., S. Abdullah, and N. S. Jaddi. 2015. Fuzzy population-based meta-heuristic approaches for attribute reduction in rough set theory. World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering 9 (12):2065–73.
- Mafarja, M., I. Aljarah, A. A. Heidari, H. Faris, P. Fournier-Viger, L. Xiaodong, and S. Mirjalili. 2018. Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowledge-Based Systems 161:185–204. doi:https://doi.org/10.1016/j.knosys.2018.08.003.
- Mafarja, M., and S. Mirjalili. 2018. Whale optimization approaches for wrapper feature selection. Applied Soft Computing 62:441–53. doi:https://doi.org/10.1016/j.asoc.2017.11.006.
- Mafarja, M., A. Qasem, A. A. Heidari, I. Aljarah, H. Faris, and S. Mirjalili. 2020. Efficient hybrid nature-inspired binary optimizers for feature selection. Cognitive Computation 12 (1):150–75. doi:https://doi.org/10.1007/s12559-019-09668-6.
- Mafarja, M. M., and S. Mirjalili. 2017. Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–12. doi:https://doi.org/10.1016/j.neucom.2017.04.053.
- McCollum, B., P. McMullan, A. J. Parkes, E. K. Burke, and S. Abdullah. 2009. “An extended great deluge approach to the examination timetabling problem.” Proceedings of the 4th multidisciplinary international scheduling: Theory and applications 2009 (MISTA 2009): 424–34, Dublin, Ireland.
- Mcmullan, P. 2007. An extended implementation of the great deluge algorithm for course timetabling. Paper read at International Conference on Computational Science, Springer, Berlin, Heidelberg, New York City.
- Meiri, R., and J. Zahavi. 2006. Using simulated annealing to optimize the feature selection problem in marketing applications. European Journal of Operational Research 171 (3):842–58. doi:https://doi.org/10.1016/j.ejor.2004.09.010.
- Mosbah, A. B., and T.-M. Dao. 2010. Optimimization of group scheduling using simulation with the meta-heuristic extended great deluge (EGD) approach. Paper read at Industrial Engineering and Engineering Management (IEEM), 2010 IEEE International Conference on, Macao, China.
- Nahas, N., A. Khatab, D. Ait-Kadi, and M. Nourelfath. 2008. Extended great deluge algorithm for the imperfect preventive maintenance optimization of multi-state systems. Reliability Engineering & System Safety 93 (11):1658–72.
- Nourelfath, M., N. Nahas, and B. Montreuil. 2007. Coupling ant colony optimization and the extended great deluge algorithm for the discrete facility layout problem. Engineering Optimization 39 (8):953–68. doi:https://doi.org/10.1080/03052150701551461.
- Pawlak, Z. 1982. Rough sets. International Journal of Computer & Information Sciences 11 (5):341–56. doi:https://doi.org/10.1007/bf01001956.
- Rodrigues, D., L. A. M. Pereira, R. Y. M. Nakamura, K. A. P. Costa, X.-S. Yang, A. N. Souza, and J. P. Papa. 2014. A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest. Expert Systems with Applications 41 (5):2250–58. doi:https://doi.org/10.1016/j.eswa.2013.09.023.
- Tahir, M. A., A. Bouridane, and F. Kurugollu. 2007. Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier. Pattern Recognition Letters 28 (4):438–46. doi:https://doi.org/10.1016/j.patrec.2006.08.016.
- Tubishat, M., M. A. M. Abushariah, N. Idris, and I. Aljarah. 2019. Improved whale optimization algorithm for feature selection in Arabic sentiment analysis. Applied Intelligence 49 (5):1688–707. doi:https://doi.org/10.1007/s10489-018-1334-8.
- Zeng, A., L. Tianrui, D. Liu, J. Zhang, and H. Chen. 2015. A fuzzy rough set approach for incremental feature selection on hybrid information systems. Fuzzy Sets and Systems 258:39–60. doi:https://doi.org/10.1016/j.fss.2014.08.014.
- Zhang, H., and G. Sun. 2002. Feature selection using tabu search method. Pattern Recognition 35 (3):701–11. doi:https://doi.org/10.1016/S0031-3203(01)00046-2.
- Zhang, Y., L. Hai-Gang, Q. Wang, and C. Peng. 2019. A filter-based bare-bone particle swarm optimization algorithm for unsupervised feature selection. Applied Intelligence 49 (8):2889–98. doi:https://doi.org/10.1007/s10489-019-01420-9.
- Zhang, Y., S. Wang, P. Phillips, and J. Genlin. 2014. Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowledge-Based Systems 64:22–31. doi:https://doi.org/10.1016/j.knosys.2014.03.015.
- Zhong, N., J. Dong, and S. Ohsuga. 2001. Using rough sets with heuristics for feature selection. Journal of Intelligent Information Systems 16 (3):199–214. doi:https://doi.org/10.1023/a:1011219601502.
- Zhu, Z., Y. S. Ong, and M. Dash. 2007. Wrapper–filter feature selection algorithm using a memetic framework. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 37 (1):70–76. doi:https://doi.org/10.1109/tsmcb.2006.883267.