Abstract
Support Vector Machine (SVM) is one of popular supervised machine learning algorithms, which can be used for both regression or classification challenges. The operation of SVM algorithm is based on finding the optimal hyperplane to discriminate between different classes. This hyperplane is known as kernel. In SVM, penalty parameter C and \(\sigma \) parameter of Radial Basis Function (RBF) can have a significant impact on the complexity and performance of SVM. Usually these parameters are randomly chosen. However, SVM is highly needed to determine the optimal parameters values to obtain expected learning performance. In this chapter, an optimization method based on optimal foraging theory is proposed to adjust the two main parameters of gaussian kernel function of SVM to increase the classification accuracy. Six well-known benchmark datasets taken from UCI machine learning data repository were employed for evaluating the proposed (OFA-SVM). In addition, the performance of the proposed optimal foraging algorithm for SVM’s parameters optimization (OFA-SVM) is compared with five other well-known and recently meta-heuristic optimization algorithms. These algorithms are Bat Algorithm (BA), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Chicken Swarm Optimization (CSO) and Particle Swarm Optimization (PSO). The experimental results show that the proposed OFA-SVM can achieve better results compared with the other algorithms. Moreover, the results demonstrate the capability of the proposed OFA-SVM in finding the optimal parameters values of RBF of SVM.
G. I. Sayed, M. Soliman, A. E. Hassanien—Scientific Research Group in Egypt (SRGE), http://www.egyptscience.net.
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References
Vapnik, V.: The nature of statistical learning theory. Informat. Sci. Stat. Springer, New York (1995)
Lin, S., Ying, K., Chen, S., Lee, Z.: Evolutionary tuning of svm parameter values in multiclass problems. Neurocomputing 71(4), 3326–3334 (2008)
Luo, Z., Zhang, W., Li, Y., Xiang, M.: Svm parameters tuning with quantum particles swarm optimization. In: IEEE Confernce on Cybernetics and Intelligent Systems, pp. 183–187, Chengdu, China (2008)
Sayed, G., Ali, M., Gaber, T., Hassanien, A., Sansel, V.: Interphase cells removal from metaphase chromosome images based on meta-heuristic grey wolf optimizer. In: 11th International Computer Engineering Conference (ICENCO). IEEE, pp. 261–266. Egypt, Cairo (2015)
Muller, K.R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.: An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12(2), 181–201 (2001)
Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Mach. Learn. 46(1–3), 131–159 (2002)
Sayed, G., Hassanien, A., Schaefer, G.: An automated computer-aided diagnosis system for abdominal ct liver images. In: The 20th Annual Conference in Medical Image Understanding and Analysis (MIUA 2016). Elsevier, vol. 90, pp. 68–73. Loughborough University, Loughborough, UK (2016)
Friedrichs, F., Igel, C.: Evolutionary tuning of multiple SVM parameters. Neurocomputing 64, 107–117 (2005)
Staelin, C.: Parameter Selection for Support Vector Machines, vol. 12 (2003)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, New York (2000)
Zhang, L., Wang, J.: Optimizing parameters of support vector machines using team-search-based particle swarm optimization. Eng. Comput. 32(5), 1194–1213 (2015)
Daubechies, I., Mallat, S., Willsky, A.S.: Introduction to the special issue on wavelet transforms and multiresolution signal analysis. IEEE Trans. Informat. Theor. 38(2), 529–532 (1992)
Mouhamed, M.R., Zawbaa, H.M., Al-Shammari, E., Hassanien, A.E., Snasel, V.: Blind watermark approach for map authentication using support vector machine. In: International Conference on Advances in Security of Information and Communication Networks, pp. 84–97 (2013)
Sinervo, B.: Optimal Foraging Theory: Constraints and Cognitive Processes, Chapter 6, pp. 105–130. Behavioral Ecology. University of California, Santa Cruz. (1997)
Krebs, J.R., Erichsen, J.T., Webber, M.I.: Optimal prey selection in the great tits (parus major). Anim. Behav. 25(1), 30–38 (1977)
Zhu, G., Zhang, W.: Optimal foraging algorithm for global optimization. 51, 294–313, 12 (2016)
Pyke, G.H., Pulliam, H.R., Charnov, E.L.: Optimal foraging: a selective review of theory and tests. Q. Rev. Biol. 52(2), 37–154 (1977)
Tharwat, A., Gabel, T., Hassanien, A.E.: Parameter optimization of support vector machine using dragonfly algorithm. In: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, pp. 309–319, Egypt (2017)
Sayed, G., Hassanien, A., Kim, T.: Interphase cells removal from metaphase chromosome images based on meta-heuristic grey wolf optimizer. 11th International Computer Engineering Conference (ICENCO). IEEE, pp. 261–266. Egypt, Cairo (2015)
Sayed, G., Darwish, A., Hassanien, A.: Quantum multiverse optimization algorithm for optimization problems. Neural Comput. Appl. 1–18 (2017)
Bache, K., Lichman, M.: UCI Machine Learning Repository. http://archive.ics.uci.edu/ml
Sayed, G., Soliman, M., Hassanien, A.: Medical Imaging in Clinical Applications, Series Studies in Computational Intelligence, volume 651, chapter Bio-inspired Swarm Techniques for Thermogram Breast Cancer Detection, pp. 487–506. Springer International Publishing Switzerland (2016)
Sayed, G., Khoriba, G., Haggag, M.: A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl. Intell. 1–20 (2018)
Wang, H., Zhang, H., Cang, S., Liao, W., Zhu, F.: Parameters optimization of classifier and feature selection based on improved artificial bee colony algorithm. In: Proceedings of the International Conference on Advanced Mechatronic Systems, pp. 242–247, Melbourne, Australia (2016)
Huang, C., Wang, C.: A GA-based feature selection and parameters optimization for support vector machines. Exp. Syst. Appl. 31(2), 231–240 (2006)
Taie, S., Ghonaim, W.: Title CSO-based algorithm with support vector machine for brain tumar’s disease diagnosis. In: IEEE International Conference on Persasive Computing and Communications Workshops, pp. 183–187, Kona, USA (2017)
Lin, S., Ying, K., Chen, S., Lee, Z.: Particle swarm optimization for parameter determination and feature selection of support vector machines. Exp. Syst. Appl. 35(4), 1817–1824 (2008)
Taie, S., Ghonaim, W.: Adjusted bat algorithm for tuning of support vector machine parameters. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2225–2232, Vancouver, Canada (2016)
Demśar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Sayed, G., Hassanien, A., Azar, A.: Feature selection via a novel chaotic crow search algorithm. Neural Comput. Appl. 1–18 (2017)
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Sayed, G.I., Soliman, M., Hassanien, A.E. (2019). Parameters Optimization of Support Vector Machine Based on the Optimal Foraging Theory. In: Hassanien, A. (eds) Machine Learning Paradigms: Theory and Application. Studies in Computational Intelligence, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-030-02357-7_15
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