Abstract
Feature selection problem from the domain of machine learning refers to selecting only those features from the high dimensional datasets, that have prominent influence on dependent variable(s). In this way, dataset dimensionallity is reduced and only the riches data is kept, training process of machine learning model becomes more efficient and accuracy is increased. This manuscript proposes a new hybridized version of the sine cosine algorithm adjusting for solving feature selection problem. Hybridization is relatively novel approach for combing and improving metaheuristics optimizer. Notwithstanding that the basic sine cosine algorithm establishes good performance for solving NP hard challenges, based on simulation results, it was concluded that there is still space for improvement in its exploitation process. Original sine cosine algorithm and proposed hybridized implementation were tested on a well-known 21 machine learning datasets retrieved from the UCL repository. Comparative analysis between hybrid sine cosine and original one, as well as with 10 other state-of-the-art metaheuristics was conducted. Established results in terms of classification accuracy and fitness prove the robustness and efficiency of proposed method for solving this type of NP hard challenge.
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References
Bacanin, N., Bezdan, T., Tuba, E., Strumberger, I., Tuba, M.: Monarch butterfly optimization based convolutional neural network design. Mathematics 8(6), 936 (2020)
Bacanin, N., Bezdan, T., Tuba, E., Strumberger, I., Tuba, M., Zivkovic, M.: Task scheduling in cloud computing environment by grey wolf optimizer. In: 2019 27th Telecommunications Forum (TELFOR), pp. 1–4. IEEE (2019)
Bacanin, N., Tuba, E., Zivkovic, M., Strumberger, I., Tuba, M.: Whale optimization algorithm with exploratory move for wireless sensor networks localization. In: Abraham, A., Shandilya, S.K., Garcia-Hernandez, L., Varela, M.L. (eds.) HIS 2019. AISC, vol. 1179, pp. 328–338. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-49336-3_33
Bacanin, N., Tuba, M.: Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint. Sci. World J. 2014, 16 (2014). Special issue Computational Intelligence and Metaheuristic Algorithms with Applications
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. AISC, vol. 1077, pp. 371–378. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0936-0_39
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: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I.U., Cebi, S., Tolga, A.C. (eds.) INFUS 2020. AISC, vol. 1197, pp. 955–963. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-51156-2_111
Bezdan, T., Zivkovic, M., Tuba, E., Strumberger, I., Bacanin, N., Tuba, M.: Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I.U., Cebi, S., Tolga, A.C. (eds.) INFUS 2020. AISC, vol. 1197, pp. 718–725. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-51156-2_83
Brajevic, I., Tuba, M., Bacanin, N.: Multilevel image thresholding selection based on the cuckoo search algorithm. In: Proceedings of the 5th International Conference on Visualization, Imaging and Simulation (VIS’12), Sliema, Malta, pp. 217–222 (2012)
Brezočnik, L., Fister jr, I., Podgorelec, V.: Swarm intelligence algorithms for feature selection: a review. Appl. Sci. 8, 1521 (2018). https://doi.org/10.3390/app8091521
Calvet, L., de Armas, J., Masip, D., Juan, A.A.: Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs. Open Math. 15(1), 261–280 (2017)
Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electric. Eng. 40(1), 16–28 (2014)
Hussien, A.G., Oliva, D., Houssein, E.H., Juan, A.A., Yu, X.: Binary whale optimization algorithm for dimensionality reduction. Mathematics 8(10) (2020). https://doi.org/10.3390/math8101821, https://www.mdpi.com/2227-7390/8/10/1821
Karaboga, D., Basturk, B.: Artificial Bee Colony (ABC) optimization algorithm for solving constrained optimization problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. LNCS (LNAI), vol. 4529, pp. 789–798. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72950-1_77
Liang, J., et al.: Problem definitions and evaluation criteria for the cec 2006 special session on constrained real-parameter optimization (2006)
Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96 (2016). https://doi.org/10.1016/j.knosys.2015.12.022
Bacanin, N., Bezdan, T., Tuba, E., Strumberger, I., Tuba, M.: Optimizing convolutional neural network hyperparameters by enhanced swarm intelligence metaheuristics. Algorithms 13(3), 67 (2020)
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: BGSA: binary gravitational search algorithm. Nat. Comput. Int. J. 9(3), 727–745 (2010)
Strumberger, I., Bacanin, N., Tuba, M., Tuba, E.: Resource scheduling in cloud computing based on a hybridized whale optimization algorithm. Appl. Sci. 9(22), 4893 (2019)
Strumberger, I., Tuba, E., Bacanin, N., Zivkovic, M., Beko, M., Tuba, M.: Designing convolutional neural network architecture by the firefly algorithm. In: 2019 International Young Engineers Forum (YEF-ECE), pp. 59–65. IEEE (2019)
Tuba, E., Strumberger, I., Zivkovic, D., Bacanin, N., Tuba, M.: Mobile robot path planning by improved brain storm optimization algorithm. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2008)
Tuba, M., Bacanin, N.: Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems. Neurocomputing 143, 197–207 (2014). https://doi.org/10.1016/j.neucom.2014.06.006
Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms. Appl. Soft Comput. 18, 261–276 (2014). https://doi.org/10.1016/j.asoc.2013.09.018, https://www.sciencedirect.com/science/article/pii/S1568494613003128
Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04944-6_14
Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12538-6_6
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: 2020 International Wireless Communications and Mobile Computing (IWCMC), pp. 1176–1181. IEEE (2020)
Zivkovic, M., et al.: Covid-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain. Cities Soc. 66, 102669 (2021)
Zivkovic, M., Bacanin, N., Zivkovic, T., Strumberger, I., Tuba, E., Tuba, M.: Enhanced grey wolf algorithm for energy efficient wireless sensor networks. In: 2020 Zooming Innovation in Consumer Technologies Conference (ZINC), pp. 87–92. IEEE (2020)
Zouache, D., Ben Abdelaziz, F.: A cooperative swarm intelligence algorithm based on quantum-inspired and rough sets for feature selection. Comput. Ind. Eng. 115, 26–36 (2018). https://doi.org/10.1016/j.cie.2017.10.025, https://www.sciencedirect.com/science/article/pii/S0360835217305107
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The paper is supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.
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Bacanin, N., Petrovic, A., Zivkovic, M., Bezdan, T., Antonijevic, M. (2021). Feature Selection in Machine Learning by Hybrid Sine Cosine Metaheuristics. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1440. Springer, Cham. https://doi.org/10.1007/978-3-030-81462-5_53
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