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Research Article

General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification

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Pages 247-263 | Received 13 Jul 2020, Accepted 04 Dec 2020, Published online: 23 Dec 2020

References

  • Aghdam, M. H., N. Ghasem-Aghaee, and M. E. Basiri. 2009. Text feature selection using ant colony optimization. Expert Systems with Applications 36 (3,Part 2):6843–53. doi:10.1016/j.eswa.2008.08.022.
  • 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. doi:10.1016/j.asoc.2018.07.040.
  • Amoozegar, M., and B. Minaei-Bidgoli. 2018. Optimizing multi-objective PSO based feature selection method using a feature elitism mechanism. Expert Systems with Applications 113:499–514. doi:10.1016/j.eswa.2018.07.013.
  • Arora, S., and S. Singh. 2018. Butterfly optimization algorithm: A novel approach for global optimization. Soft Computing. doi:10.1007/s00500-018-3102-4.
  • Banka, H., and S. Dara. 2015. A Hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation. Pattern Recognition Letters 52 (SupplementC):94–100. doi:10.1016/j.patrec.2014.10.007.
  • Barani, F., M. Mirhosseini, and H. Nezamabadi-pour. 2017. Application of binary quantum-inspired gravitational search algorithm in feature subset selection. Applied Intelligence 47 (2):304–18. doi:10.1007/s10489-017-0894-3.
  • Bhadra, T., and S. Bandyopadhyay. 2015. Unsupervised feature selection using an improved version of Differential Evolution. Expert Systems with Applications 42 (8):4042–53. doi:10.1016/j.eswa.2014.12.010.
  • Chuang, L.-Y., H.-W. Chang, C.-J. Tu, and C.-H. Yang. 2008. Improved binary PSO for feature selection using gene expression data. Computational Biology and Chemistry 32 (1):29–38. doi:10.1016/j.compbiolchem.2007.09.005.
  • Emary, E., H. M. Zawbaa, and A. E. Hassanien. 2016. Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–81. doi:10.1016/j.neucom.2015.06.083.
  • Faramarzi, A., M. Heidarinejad, B. Stephens, and S. Mirjalili. 2020. Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems 191:105190. doi:10.1016/j.knosys.2019.105190.
  • Faris, H., M. M. Mafarja, A. A. Heidari, I. Aljarah, A. M. Al-Zoubi, S. Mirjalili, and H. Fujita. 2018. An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems. Knowledge-Based Systems 154:43–67. doi:10.1016/j.knosys.2018.05.009.
  • Hammouri, A. I., M. Mafarja, M. A. Al-Betar, M. A. Awadallah, and I. Abu-Doush. 2020. An improved Dragonfly Algorithm for feature selection. Knowledge-Based Systems 203:106131. doi:10.1016/j.knosys.2020.106131.
  • Hu, L., W. Gao, K. Zhao, P. Zhang, and F. Wang. 2018. Feature selection considering two types of feature relevancy and feature interdependency. Expert Systems with Applications 93:423–34. doi:10.1016/j.eswa.2017.10.016.
  • Ibrahim, R. A., A. A. Ewees, D. Oliva, M. Abd Elaziz, and S. Lu. 2019. Improved salp swarm algorithm based on particle swarm optimization for feature selection. Journal of Ambient Intelligence and Humanized Computing 10 (8):3155–69. doi:10.1007/s12652-018-1031-9.
  • Kashef, S., and H. Nezamabadi-pour. 2015. An advanced ACO algorithm for feature subset selection. Neurocomputing 147:271–79. doi:10.1016/j.neucom.2014.06.067.
  • Kaur, T., B. S. Saini, and S. Gupta. 2018. A novel feature selection method for brain tumor MR image classification based on the Fisher criterion and parameter-free Bat optimization. Neural Computing and Applications 29 (8):193–206. doi:10.1007/s00521-017-2869-z.
  • Kennedy, J. 2011. Particle Swarm Optimization. In Encyclopedia of Machine Learning, ed. Claude S., Geoffrey I. W.,760–66. Boston, MA: Springer. doi:10.1007/978-0-387-30164-8_630.
  • Kira, K., and L. A. Rendell. 1992. The feature selection problem: Traditional methods and a new algorithm. Proceedings of the Tenth National Conference on Artificial Intelligence, San Jose, California, 129–34.
  • Krömer, P., J. Platoš, J. Nowaková, and V. Snášel. 2018. Optimal column subset selection for image classification by genetic algorithms. Annals of Operations Research 265 (2):205–22. doi:10.1007/s10479-016-2331-0.
  • Li, C., X. Luo, Y. Qi, Z. Gao, and X. Lin. 2020. A new feature selection algorithm based on relevance, redundancy and complementarity. Computers in Biology and Medicine 119:103667. doi:10.1016/j.compbiomed.2020.103667.
  • Liang, J. J., A. K. Qin, P. N. Suganthan, and S. Baskar. 2006. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation 10 (3):281–95. doi:10.1109/TEVC.2005.857610.
  • Lu, L., J. Yan, and C. W. de Silva. 2015. Dominant feature selection for the fault diagnosis of rotary machines using modified genetic algorithm and empirical mode decomposition. Journal of Sound and Vibration 344:464–83. doi:10.1016/j.jsv.2015.01.037.
  • Mafarja, M., I. Aljarah, H. Faris, A. I. Hammouri, A. M. Al-Zoubi, and S. Mirjalili. 2019. Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Systems with Applications 117:267–86. doi:10.1016/j.eswa.2018.09.015.
  • Mirjalili, S. 2016. SCA: A Sine Cosine Algorithm for solving optimization problems. Knowledge-Based Systems 96:120–33. doi:10.1016/j.knosys.2015.12.022.
  • Mirjalili, S., S. M. Mirjalili, and A. Lewis. 2014. Grey Wolf Optimizer. Advances in Engineering Software 69:46–61. doi:10.1016/j.advengsoft.2013.12.007.
  • Pashaei, E., and N. Aydin. 2017. Binary black hole algorithm for feature selection and classification on biological data. Applied Soft Computing 56:94–106. doi:10.1016/j.asoc.2017.03.002.
  • Sayed, S., M. Nassef, A. Badr, and I. Farag. 2019. A Nested Genetic Algorithm for feature selection in high-dimensional cancer Microarray datasets. Expert Systems with Applications 121:233–43. doi:10.1016/j.eswa.2018.12.022.
  • Sindhu, R., R. Ngadiran, Y. M. Yacob, N. A. H. Zahri, and M. Hariharan. 2017. Sine–cosine algorithm for feature selection with elitism strategy and new updating mechanism. Neural Computing and Applications 28 (10):2947–58. doi:10.1007/s00521-017-2837-7.
  • Too, J., and A. R. Abdullah. 2020a. Opposition based competitive grey wolf optimizer for EMG feature selection. Evolutionary Intelligence. doi:10.1007/s12065-020-00441-5.
  • Too, J., and A. R. Abdullah. 2020b. A new and fast rival genetic algorithm for feature selection. The Journal of Supercomputing 1–31. doi:10.1007/s11227-020-03378-9.
  • Wang, M., Y. Wan, Z. Ye, and X. Lai. 2017. Remote sensing image classification based on the optimal support vector machine and modified binary coded ant colony optimization algorithm. Information Sciences 402:50–68. doi:10.1016/j.ins.2017.03.027.
  • Yamada, M., J. Tang, J. Lugo-Martinez, E. Hodzic, R. Shrestha, A. Saha, H. Ouyang, D. Yin, H. Mamitsuka, C. Sahinalp, et al. 2018. Ultra high-dimensional nonlinear feature selection for big biological data. IEEE Transactions on Knowledge and Data Engineering 30 (7):1352–65. doi:10.1109/TKDE.2018.2789451.
  • Zhang, L., K. Mistry, C. P. Lim, and S. C. Neoh. 2018. Feature selection using firefly optimization for classification and regression models. Decision Support Systems 106:64–85. doi:10.1016/j.dss.2017.12.001.
  • Zhang, L., L. Shan, and J. Wang. 2017. Optimal feature selection using distance-based discrete firefly algorithm with mutual information criterion. Neural Computing and Applications 28 (9):2795–808. doi:10.1007/s00521-016-2204-0.
  • Zhang, Y., D. Gong, Y. Hu, and W. Zhang. 2015. Feature selection algorithm based on bare bones particle swarm optimization. Neurocomputing 148:150–57. doi:10.1016/j.neucom.2012.09.049.

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