Abstract
Feature selection is considered one of the challenging machine learning tasks. Selecting a subset of relevant features can significantly influence on the classification accuracy and computational time of any machine learning algorithm. This paper introduces a novel wrapper-based feature selection algorithm based on using Equilibrium Optimizer (EO) algorithm and chaos theory. The principles of chaos theory is used to overcome the slow convergence rate and the entrapment in local optima problems of the original EO. Thus, ten different chaotic maps are embedded in the optimization process of EO to overcome these problems and achieve a more effective and robust search mechanism. Also, eight different S-shaped and V-shaped transfer functions are employed. The performance of the proposed hybrid algorithm is tested on fifteen benchmark datasets and four other large scale NLP datasets collected from the UCI machine learning repository. The experimental results showed the capability of the proposed hybrid algorithm. Moreover, the results proved that the proposed hybrid algorithm is a higly competitive algorithm and can find the optimal feature subset, which minimizes the number of selected features while maximizes the classification accuracy.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Abbas N, Aftan H (2014) Quantum artificial bee colony algorithm for numerical function optimization. Int J Comput Appl 93(9):28–30. https://doi.org/10.5120/16244-5800
Abdallh G, Algamal Z (2020) A qsar classification model of skin sensitization potential based on improving binary crow search algorithm. Electron J Appl Stat Anal 13(1):86–95. https://doi.org/10.1285/i20705948v13n1p86
Abualigah L (2019) Feature selection and enhanced Krill herd algorithm for text document clustering, 1st edn. Studies in computational intelligence. Springer, Warsaw
Abualigah L, Khader A, Hanandeh E (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466. https://doi.org/10.1016/j.jocs.2017.07.018
Abualigah L, Khader A, Hanandeh E (2019) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071. https://doi.org/10.1007/s10489-018-1190-6
Adewumi A, Kagamba J, Alochukwu A (2016) Application of chaos theory in the prediction of motorised traffic flows on urban networks. Math Prob Eng 2016:1–15. https://doi.org/10.1155/2016%2F5656734
Al-Fakih A, Algamal Z, Lee M, Aziz M, Ali H (2019a) Qsar classification model for diverse series of antifungal agents based on improved binary differential search algorithm. SAR QSAR Environ Res 30(2):131–143. https://doi.org/10.1080/1062936X.2019.1568298
Al-Fakih A, Algamal Z, Lee M, Aziz M, Ali H (2019b) A qsar model for predicting antidiabetic activity of dipeptidyl peptidase-iv inhibitors by enhanced binary gravitational search algorithm. SAR QSAR Environ Res 30(6):403–416. https://doi.org/10.1080/1062936X.2019.1607899
Algamal Z, Qasim M, Lee M, Ali H (2020a) Qsar model for predicting neuraminidase inhibitors of influenza a viruses (h1n1) based on adaptive grasshopper optimization algorithm. SAR QSAR Environ Res 31(11):803–814. https://doi.org/10.1080/1062936X.2020.1818616
Algamal Z, Qasim M, Lee M, Mohammad Ali H (2020b) High-dimensional qsar/qspr classification modeling based on improving pigeon optimization algorithm. Chemom Intell Lab Syst 206:1–6. https://doi.org/10.1016/j.chemolab.2020.104170
Anter A, Ali M (2019) Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems. Soft Comput. https://doi.org/10.1007/s00500-019-03988-3
Arora S, Anand P (2018) Binary butterfly optimization approaches for feature selection. Expert Syste Appl 116:1–25. https://doi.org/10.1016/j.eswa.2018.08.051
Arora S, Singh S (2018) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput. https://doi.org/10.1007/s00500-018-3102-4
Askar S, Karawia A, Al-Khedhairi A, Al-Ammar F (2019) An algorithm of image encryption using logistic and two-dimensional chaotic economic maps. Entropy 21(44):1–17. https://doi.org/10.3390/e21010044
Ateya A, Muthanna A, Vybornova A, Algarni A, Abuarqoub A, Koucheryavy Y, Koucheryavy A (2019) Chaotic salp swarm algorithm for sdn multi-controller networks. Int J Eng Sci Technol. https://doi.org/10.1016/j.jestch.2018.12.015
Barani F, Nezamabadi-pour H (2018) Bqiabc: a new quantum-inspired artificial bee colony algorithm for binary optimization problems. J AI Data Min 6(1):133–143. https://doi.org/10.22044/JADM.2017.899
Briggs J, Peat F (1989) Turbulent mirror: an illustrated guide to chaos theory and the science of wholeness, 1st edn. Harper & Row, New York
Coelho L, Mariani V (2008) Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert Syst Appl 34:1905–1913. https://doi.org/10.1016/j.eswa.2007.02.002
Curry D (2012) Practical application of chaos theory to systems engineering. Procedia Comput Sci 8:39–44. https://doi.org/10.1016/j.procs.2012.01.011
Derrac J, Molina G, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1:3–18. https://doi.org/10.1016/j.swevo.2011.02.002
Digalakis J, Margaritis K (2001) On benchmarking functions for genetic algorithms. Int J Comput Math 77:481–506. https://doi.org/10.1080/00207160108805080
Duffy J (2000) The application of chaos theory to the career-plateaued worker. J Employ Counsel 37(4):229–236. https://doi.org/10.1002/j.2161-1920.2000.tb01029.x
Eggleston D (2018) Application of chaos theory to the particle dynamics of asymmetry-induced transport. Phys Plasmas 25:1–30. https://doi.org/10.1063/1.5017710
Elaziz M, Nabil N, Ewees A, Lu S (2019) Automatic data clustering based on hybrid atom search optimization and sine-cosine algorithm. In: 2019 IEEE Congress on Evolutionary Computation (CEC). New Zealand. pp 2315–2322
Emary E, Zawbaa H, Hassanien A (2016) Binary ant lion approaches for feature selection. Neurocomputing 213:54–65. https://doi.org/10.1016/j.neucom.2016.03.101
Fallahi K, Raoufi R, Khoshbin H (2008) An application of chen system for secure chaotic communication based on extended kalman filter and multi-shift cipher algorithm. Commun Nonlinear Sci Num Simul 13:763–781. https://doi.org/10.1016/j.cnsns.2006.07.006
Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2019) Equilibrium optimizer: a novel optimization algorithm. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2019.105190
Feng F, Li X (2018) Application of improved chaos theory genetic multi feature matching algorithm in patent retrieval. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-1009-7
Hafez A, Zawbaa H, Emary E, Mahmoud H, Hassanien A (2015) An innovative approach for feature selection based on chicken swarm optimization. In: 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR). pp 19–24
Kahya M, Altamir S, Algamal Z (2021) Improving whale optimization algorithm for feature selection with a time-varying transfer function. Num Algebra Control Optimiz 11(1):87–98. https://doi.org/10.3934/naco.2020017
Kaur G, Arora S (2018) Chaotic whale optimization algorithm. J Comput Design Eng 5(3):275–284. https://doi.org/10.1016/j.jcde.2017.12.006
Kennedy J, Eberhart R (1995) Particle swarm optimization. IEEE Int Conf Neural Netw 4:1942–1948
Koziel S, Michalewicz Z (2018) An improved chaotic firefly algorithm for global numerical optimization. Int J Comput Intell Syst 12(1):131–148. https://doi.org/10.2991/ijcis.2018.25905187
Lahey R (1991) An application of fractal and chaos theory in the field of two-phase flow & heat transfer. Heat Mass Transf 26(6):351–363. https://doi.org/10.1007/bf01591668
Li B, Liu C, Wu H, Zhao Y, Dong Y (2019a) Chaotic adaptive butterfly mating optimization and its applications in synthesis and structure optimization of antenna arrays. Int J Antennas Propag 2019:1–14. https://doi.org/10.1155/2019%2F1730868
Li R, Jiang Z, Li A, Yu S, Ji C (2019b) An improved shuffled frog leaping algorithm and its application in the optimization of cascade reservoir operation. Hydrol Sci J 63:2020–2034
Liu X, Shang L (2013) A fast wrapper feature subset selection method based on binary particle swarm optimization. 2013 IEEE Congress on Evolutionary Computation. Cancun, Mexico, pp 3347–3353
Meng X, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. Advances in Swarm intelligence. Springer International Publishing, Cham, pp 86–94
Mirjalili S, Lewis A (2013) S-shaped versus v-shaped transfer functions for binary particle swarm optimization. Swarm Evolut Comput 9:1–14. https://doi.org/10.1016/j.swevo.2012.09.002
Mirjalili S, Gandomi A, Mirjalili S, Saremi S, Faris H, Mirjalili S (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
Misaghi M, Yaghoobi M (2019) Improved invasive weed optimization algorithm (iwo) based on chaos theory for optimal design of pid controller. J Comput Design Eng. https://doi.org/10.1016/j.jcde.2019.01.001
Naoto I, Tomoyuki E (2014) A study on the application of chaos theory to the airflow characterization technique. J Environ Eng (Transactions of AIJ) 79(702):725–730. https://doi.org/10.3130/aije.79.725
Qasim O, Algamal Z (2020) Feature selection using different transfer functions for binary bat algorithm. Int J Math Eng Manage Sci 5(4):697–706. https://doi.org/10.33889/IJMEMS.2020.5.4.056
Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Appl 25:1077–1097. https://doi.org/10.1007/s00521-014-1597-x
Sayed G, Khoriba G, Haggag M (2018) A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl Intell 48:3462–3481. https://doi.org/10.1007/s10489-018-1158-6
Sayed G, Tharwat A, Hassanien A (2019) Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection. Appl Intell 49:188–205
Seyedali M (2015) The ant lion optimizer. Adv Eng Softw 83:80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010
Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization. Tech. rep.. Nanyang Technological University. Singapore. https://www.bibsonomy.org/bibtex/299405a8d8d87059c09ee2f6d3e740a64/schaul
Sun Y, Gao Y, Shi X (2019) Chaotic multi-objective particle swarm optimization algorithm incorporating clone immunity. Mathematics 7:1–16. https://doi.org/10.3390/math7020146
Wang B, Li W, Chen X, Chen H (2019) Improved chicken swarm algorithms based on chaos theory and its application in wind power interval prediction. Math Prob Eng 2019:1–10. https://doi.org/10.1155/2019%2F1240717
Yang XS (2010) Test problems in optimization. Wiley, Hoboken
Zhao W, Wang L, Zhang Z (2018) A novel atom search optimization for dispersion coefficient estimation in groundwater. Fut Gener Comput Syst. https://doi.org/10.1016/j.future.2018.05.037
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix A. List of benchmark functions
Appendix A. List of benchmark functions
Rights and permissions
About this article
Cite this article
Sayed, G.I., Khoriba, G. & Haggag, M.H. A novel Chaotic Equilibrium Optimizer Algorithm with S-shaped and V-shaped transfer functions for feature selection. J Ambient Intell Human Comput 13, 3137–3162 (2022). https://doi.org/10.1007/s12652-021-03151-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-021-03151-7