The fast-growing quantity of information hinders the process of machine learning, making it computationally costly and with substandard results. Feature selection is a pre-processing method for obtaining the optimal subset of features in a data set. Optimization algorithms struggle to decrease the dimensionality while retaining accuracy in high-dimensional data set. This article proposes a novel chaotic opposition fruit fly optimization algorithm, an improved variation of the original fruit fly algorithm, advanced and adapted for binary optimization problems. The proposed algorithm is tested on ten unconstrained benchmark functions and evaluated on twenty-one standard datasets taken from the Univesity of California, Irvine repository and Arizona State University. Further, the presented algorithm is assessed on a coronavirus disease dataset, as well. The proposed method is then compared with several well-known feature selection algorithms on the same datasets. The results prove that the presented algorithm predominantly outperform other algorithms in selecting the most relevant features by decreasing the number of utilized features and improving classification accuracy.

Department of Applied Cybernetics Faculty of Science University of Hradec Kràlové Hradec Kràalové Czech Republic

Department of Computational Mathematics Science and Engineering Michigan State University East Lansing MI United States of America

Department of Mathematics Faculty of Science Mansoura University Mansoura Egypt

Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia

Faculty of Informatics and Computing Singidunum University Belgrade Serbia

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Carbonell J.G., Michalski R.S., Mitchell T.M., An overview of machine learning. Machine learning (1983), 3–23. doi: 10.1016/B978-0-08-051054-5.50005-4 DOI

Caruana R., Niculescu-Mizil A., An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd international conference on Machine learning, (2006), 161–168.

Gerard G., Trunk V., A problem of dimensionality: A simple example. IEEE Transactions on pattern analysis and machine intelligence 3 (1979), 306–307. PubMed

van der Maaten L., Postma E., and van den Herik J., Dimensionality reduction: a comparative. J Mach Learn Res 10, 13, (2009), 66–71.

Levine M.D., Feature extraction: A survey. Proc. IEEE 57, 8 (1969), 1391–1407. doi: 10.1109/PROC.1969.7277 DOI

Chandrashekar G., Sahin F., A survey on feature selection methods. Computers and Electrical Engineering 40, 1 (2014), 16–28. doi: 10.1016/j.compeleceng.2013.11.024 DOI

Wolpert D. H., Macready W. G., No free lunch theorems for optimization. IEEE Trans. Evol. Comput., 1 (1997), 67–82. doi: 10.1109/4235.585893 DOI

Colorni A., Dorigo M., Maniezzo M., Distributed optimization by ant colonies. Proc. 1st Eur. Conf. Artif. Life, (1991), 134–176.

Mirjalili S., Lewis A., The whale optimization algorithm. Adv. Eng. Softw., 95, (2016), 51–67. doi: 10.1016/j.advengsoft.2016.01.008 DOI

Faris H., Aljarah I., Al-Betar M.A., Mirjalili S., Grey wolf optimizer: A review of recent variants and applications. Neural Comput. Appl., 30, 2, (2018), 413–435. doi: 10.1007/s00521-017-3272-5 DOI

Dervis K., Basturk B., A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. J. Global Optim., 39, 3, (2007), 459–471. doi: 10.1007/s10898-007-9149-x DOI

Saremi S., Mirjalili S., Lewis A., Grasshopper optimization algorithm: Theory and application. Adv. Eng. Softw., 105, (2017), 30–47. doi: 10.1016/j.advengsoft.2017.01.004 DOI

Kennedy J. and Eberhart R., Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw., 4, (2002), 1942–1948. doi: 10.1109/ICNN.1995.488968 DOI

Mirjalili S., Gandomi A.H., Mirjalili S.Z., Saremi S., Faris H., Mirjalili S.M., Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Adv. Eng. Softw., 114, (2017), 163–191. doi: 10.1016/j.advengsoft.2017.07.002 DOI

Bezdan T., Zivkovic M., Tuba E., Strumberger I., Bacanin N., Tuba M., Glioma Brain Tumor Grade Classification from MRI Using Convolutional Neural Networks Designed by Modified FA. In International Conference on Intelligent and Fuzzy Systems. Springer, (2020), 955–963.

Zivkovic M., Bacanin N., Venkatachalam K., Nayyar A., Djordjevic A., Strumberger I., Al-Turjman F., COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustainable Cities and Society, 66, (2021), 102669. doi: 10.1016/j.scs.2020.102669 PubMed DOI PMC

Bacanin N., Bezdan T., Tuba E., Strumberger I., Tuba M., Monarch butterfly optimization based convolutional neural network design. Mathematics 8(6), 936, (2020). doi: 10.3390/math8060936 DOI

Bezdan T., Tuba E., Strumberger I., Bacanin N., Tuba M., Automatically designing convolutional neural network architecture with artificial flora algorithm. In: Tuba M., Akashe S., Joshi A. (eds.) ICT Systems and Sustainability. Springer Singapore, (2020), 371–378.

Bacanin N., Bezdan T., Tuba E., Strumberg I., Tuba M., Optimizing convolutional neural network hyperparameters by enhanced swarm intelligence metaheuristics. Algorithms 13(3), 67 (2020). doi: 10.3390/a13030067 DOI

Strumberger I., Tuba E., Bacanin N., Zivkovic M., Beko M. Tuba M., Designing convolutional neural network architecture by the firefly algorithm. In: International Young Engineers Forum (YEF-ECE), (2019), 59–65. doi: 10.1109/YEF-ECE.2019.8740818 DOI

Bacanin N., Tuba E., Zivkovic M., Strumberger I., Tuba M., Whale optimization algorithm with exploratory move for wireless sensor networks localization. In: International Conference on Hybrid Intelligent Systems. Springer, (2019), 328–338.

Zivkovic M., Bacanin N., Tuba E., Strumberger I., Bezdan T., Tuba M., Wireless sensor networks life time optimization based on the improved firefly algorithm. In: International Wireless Communications and Mobile Computing (IWCMC), IEEE, (2020), 1176–1181.

Zivkovic M., Bacanin N., Zivkovic T., Strumberger I., Tuba E., Tuba M., Enhanced grey wolf algorithm for energy efficient wireless sensor networks. In: Zooming Innovation in Consumer Technologies Conference (ZINC), IEEE, (2020), 87–92.

Bacanin N., Bezdan T., Tuba E., Strumberger I., Tuba M., Zivkovic M., Task scheduling in cloud computing environment by grey wolf optimizer. In: 9 27th Telecommunications Forum (TELFOR), IEEE, (2019), 1–4.

Bezdan T., Zivkovic M., Tuba E., Strumberger I., Bacanin N., Tuba M., Multiobjective task scheduling in cloud computing environment by hybridized bat algorithm. In: International Conference on Intelligent and Fuzzy Systems, Springer, (2020), 718–725.

Strumberger I., Bacanin N., Tuba M., Tuba E., Resource scheduling in cloud computing based on a hybridized whale optimization algorithm. Applied Sciences 9(22), (2019), 4893. doi: 10.3390/app9224893 DOI

Sharma M., Kaur P., A comprehensive analysis of natureinspired meta-heuristic techniques for feature selection problem. Archives of Computational Methods in Engineering (2020), 1–25.

Shaban W.M., Rabie A.H., Saleh A.I., Abo-Elsoud M.A., A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier. Knowl.-Based Syst. 205, (2020), 106270. doi: 10.1016/j.knosys.2020.106270 PubMed DOI PMC

Jain G., Mittal D., Thakur D., Mittal M.K., A deep learning approach to detect Covid-19 coronavirus with X-ray images. Biocybern. Biomed. Eng. 40, (2020), 1391–1405. doi: 10.1016/j.bbe.2020.08.008 PubMed DOI PMC

Tuncer T., Dogan S., Ozyurt F., An automated residual exemplar local binary pattern and iterative relieff based COVID-19 detection method using chest X-ray image. Chemom. Intell. Lab. Syst. 203, (2020), 104054. doi: 10.1016/j.chemolab.2020.104054 PubMed DOI PMC

Brezočnik L., Fister I. jr, Podgorelec V., Swarm intelligence algorithms for feature selection: A review. Applied Sciences 8, (2018), 1521. doi: 10.3390/app8091521 DOI

Xue B., Zhang M., Browne W.N., Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms. Applied Soft Computing 18, (2014), 261–276. doi: 10.1016/j.asoc.2013.09.018 DOI

Zouache D., Ben Abdelaziz F., A cooperative swarm intelligence algorithm based on quantum-inspired and rough sets for feature selection. Computers and Industrial Engineering 115, (2018), 26–36. doi: 10.1016/j.cie.2017.10.025 DOI

Kennedy J., Eberhart R.C., A discrete binary version of the particle swarm algorithm. In: Proceedings of IEEE International Conference on Computational Cybernetics and Simulation, Orlando, (1997), 4104–4108.

Mirjalili S., Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27, (2016), 1053–1073. doi: 10.1007/s00521-015-1920-1 DOI

Mafarja M., Aljarah I., Heidari A.A., Faris H., Fournier-Viger P., Li X., Mirjalili S., Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowl.-Based Syst. 161, (2018), 185–204. doi: 10.1016/j.knosys.2018.08.003 DOI

Too J., Mirjalili S., A Hyper Learning Binary Dragonfly Algorithm for Feature Selection: A COVID-19 Case Study. Knowledge-Based Systems 212, (2021), 106553. doi: 10.1016/j.knosys.2020.106553 DOI

Hancer E., Xue B., Karaboga D., Zhang M., A binary ABC algorithm based on advanced similarity scheme for feature selection. Appl. Soft Comput. 36 (2015) 334–348. doi: 10.1016/j.asoc.2015.07.023 DOI

Mafarja M.M., Ajiarah I., Faris H., Hammouri A.I., Al-Zoubi A.M., Mirjalili S., Binary grasshopper optimization algorithm approaches for feature selection problems. Expert Syst. Appl. 117, (2019), 267–286. doi: 10.1016/j.eswa.2018.09.015 DOI

Pan W.T., A new evolutionary computation approach: fruit fly optimization algorithm. Conference of Digital Technology and Innovation Management, (2011), 382–391.

Pan W.T., A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26, (2012), 69–74. doi: 10.1016/j.knosys.2011.07.001 DOI

H. R. Tizhoosh, Opposition-Based Learning: A New Scheme for Machine Intelligence. International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06), (2005), 695–701.

Elaziz M.A., Diego Oliva, Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm. Energy Conversion and Management, 171, (2018), 1843–1859. doi: 10.1016/j.enconman.2018.05.062 DOI

Lu H.J., Zhang H.M., and Ma L.H., A new optimization algorithm based on chaos. J. Zhejiang Univ.-Sci. A, 7, 4, (2006), 539–542. doi: 10.1631/jzus.2006.A0539 DOI

Sayed G. I., Hassanien A. E., Azar A. T., Feature selection via a novel chaotic crow search algorithm. Neural Comput. Appl., 31, 1, (2019), 171–188. doi: 10.1007/s00521-017-2988-6 DOI

Kaur G. and Arora S., Chaotic whale optimization algorithm. J. Comput. Des. Eng., 5, 3, (2018), 275–284.

Kohli M., Arora S., Chaotic grey wolf optimization algorithm for constrained optimization problems. J. Comput. Des. Eng., 5, 4, (2018), 458–472.

Arora S. and Anand P., Chaotic grasshopper optimization algorithm for global optimization. Neural Comput. Appl., 31, 8, (2019), 4385–4405. doi: 10.1007/s00521-018-3343-2 DOI

Yu H., Zhao N., Wang P., Chen H., Li C., Chaos-enhanced synchronized bat optimizer. Applied Mathematical Modelling, 77, (2020), 1201–1215. doi: 10.1016/j.apm.2019.09.029 DOI

Price K., Awad N., Ali M., Suganthan P., Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization. In Technical Report; Nanyang Technological University, 2018.

Wang G.G., Deb S., Gao X.Z., Coelho L.D.S., A new metaheuristic optimisation algorithm motivated by elephant herding behaviour. International Journal of Bio-Inspired Computation 8, (2016), 394–409. doi: 10.1504/IJBIC.2016.081335 DOI

Muthusamy H., Ravindran S., Saacob S., Polat K., An improved elephant herding optimization using sine–cosine mechanism and opposition based learning for global optimization problems. Expert Systems with Applications, 172, (2021), 114607. doi: 10.1016/j.eswa.2021.114607 DOI

Mirjalili S., SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based systems, 96, (2016), 120–133. doi: 10.1016/j.knosys.2015.12.022 DOI

Mirjalili S., Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, (2015), 228–249. doi: 10.1016/j.knosys.2015.07.006 DOI

Simon D., Biogeography-based optimization. IEEE transactions on evolutionary computation, 12, (2008), 702–713. doi: 10.1109/TEVC.2008.919004 DOI

Friedman M., The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the american statistical association, 32, (1937), 675–701. doi: 10.1080/01621459.1937.10503522 DOI

Friedman M., A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics, 11, (1940), 86–92. doi: 10.1214/aoms/1177731944 DOI

Iman R.L., Davenport J.M., Approximations of the critical region of the fbietkan statistic. Communications in Statistics-Theory and Methods, 9, (1980), 571–595. doi: 10.1080/03610928008827904 DOI

Sheskin D.J., Handbook of parametric and nonparametric statistical procedures. Chapman and Hall/CRC, (2020).

Derrac J., Garcıa S., Molina D., Herrera F., A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1, 1, (2011), 3–18. doi: 10.1016/j.swevo.2011.02.002 DOI

UCI Machine Learning Repository, https://archive.ics.uci.edu/ml/index.php, (Accessed 14 April 2019).

Datasets—Feature Selection @ ASU, http://featureselection.asu.edu/datasets.php, (Accessed 9 November 2019)

Neggaz N., Houssein E.H., Hussain K., An efficient henry gas solubility optimization for feature selection. Expert Syst. Appl. 152, (2020), 113364. doi: 10.1016/j.eswa.2020.113364 DOI

Al-Madi N., Faris H., Mirjalili S., Binary multi-verse optimization algorithm for global optimization and discrete problems. Int. J. Mach. Learn. Cybern. (2019). doi: 10.1007/s13042-019-00931-8 DOI

He Y., Xie H., Wong T.L., Wang X., A novel binary artificial bee colony algorithm for the set-union knapsack problem. Future Gener. Comput. Syst. 78, (2018), 77–86. doi: 10.1016/j.future.2017.05.044 DOI

Tanabe R., Fukunaga A.S., Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE Congress on Evolutionary Computation, CEC, (2014), 1658–1665. doi: 10.1109/CEC.2014.6900380 DOI

Sayed G.I., Hassanien A.E., Azar A.T., Feature selection via a novel chaotic crow search algorithm. Neural Comput. App.l, (2017), 1–18.

Pierezan J., Dos L. Santos Coelho, Coyote optimization algorithm: A new metaheuristic for global optimization problems. In: 2018 IEEE Congress on Evolutionary Computation, CEC, (2018), 1–8.

Thom de Souza R.C., de Macedo C.A., dos Santos Coelho L., et al.., Binary coyote optimization algorithm for feature selection. Pattern Recognit, 107, (2020), 107470. doi: 10.1016/j.patcog.2020.107470 DOI

Carrasco J., García S., Rueda M.M., et al.., Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review. Swarm Evol. Comput. 54, (2020), 100665. doi: 10.1016/j.swevo.2020.100665 DOI

Chen X., Tang Y., Mo Y., et al.., A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study, Eur. Radiol. (2020). doi: 10.1007/s00330-020-06829-2 PubMed DOI PMC

Coronavirus Update (Live)- Worldometer, https://www.worldometers.info/coronavirus/, (Accessed 5 August 2021).

Sahlol A.T., Yousri D., Ewees A.A., AlQaness M.A.A., Damasevicius Ro., Abd Elaziz M., COVID-19 image classification using deep features and fractional-order marine predators algorithm. Sci. Rep. (2020). doi: 10.1038/s41598-020-71294-2 PubMed DOI PMC

Iwendi C., Bashir A.K., Peshkar A., et al.., COVID-19 patient health prediction using boosted random forest algorithm. Front. Publ. Health, 8, (2020). doi: 10.3389/fpubh.2020.00357 PubMed DOI PMC

Quasi-reflection learning arithmetic optimization algorithm firefly search for feature selection

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