Abstract
Support Vector Machine (SVM) is one of the widely used algorithms for classification and regression problems. In SVM, penalty parameter C and kernel parameters can have a significant impact on the complexity and performance of SVM. In this paper, an Optimal Foraging Algorithm (OFA) is proposed to optimize the main parameters of SVM and reduce the classification error. Six public benchmark datasets were employed for evaluating the proposed (OFA-SVM). Also, five well-known and recently optimization algorithms are used for evaluation. These algorithms are Artificial Bee Colony (ABC), Genetic Algorithm (GA), Chicken Swarm Optimization (CSO), Particle Swarm Optimization (PSO) and Bat Algorithm (BA). The experimental results show that the proposed OFA-SVM obtained superior results. Also, the results demonstrate the capability of the proposed OFA-SVM to find optimal values of SVM parameters.
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References
Sayed, G., Hassanien, A.: Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images. Appl. Intell. 47(2), 397–408 (2017)
Sayed, G., Hassanien, A., Ibrahim, M.: Particle swarm optimization and k-means algorithm for chromosomes extraction from metaphase images. In: 3rd International Conference on Advanced Intelligent Systems and Informatics (AISI 2017), Cairo, Egypt, pp. 331–341. Springer (2017)
Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Mach. Learn. 46(1–3), 131–159 (2002)
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 conference on Cybernetics and Intelligent Systems, Chengdu, China, pp. 183–187 (2008)
Friedrichs, F., Igel, C.: Evolutionary tuning of multiple SVM parameters. Neurocomputing 64, 107–117 (2005)
Zhang, L., Wang, J.: Optimizing parameters of support vector machines using team-search-based particle swarm optimization. Eng. Comput. 32(5), 1194–1213 (2015)
Sinervo, B.: Optimal foraging theory: constraints and cognitive processes. In: Behavioral Ecology, chap. 6, pp. 105–130. 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 for aging algorithm for global optimization. Appl. Soft Comput. 51, 294–313 (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.: Parameter optimization of support vector machine using dragonfly algorithm. In: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, Egypt, pp. 309–319 (2017)
Sayed, G., Hassanien, A., Kim, T.: Interphase cells removal from metaphase chromosome images based on meta-heuristic grey wolf optimizer. In: 11th International Computer Engineering Conference (ICENCO), Cairo, Egypt, pp. 261–266. IEEE (2015)
Bache, K., Lichman, M.: UCI Machine Learning Repository. http://archive.ics.uci.edu/ml
Sayed, G., Soliman, M., Hassanien, A.: Bio-inspired swarm techniques for thermogram breast cancer detection. In: Medical Imaging in Clinical Applications. Studies in Computational Intelligence, vol. 651, pp. 487–506. Springer, Cham (2016)
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, Melbourne, Australia, pp. 242–247 (2016)
Huang, C., Wang, C.: A GA-based feature selection and parameters optimization for support vector machines. Expert 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, Kona, USA, pp. 183–187 (2017)
Lin, S., Ying, K., Chen, S., Lee, Z.: Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert 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), Vancouver, Canada, pp. 2225–2232 (2016)
Sayed, G., Hassanien, A., Azar, A.: Feature selection via a novel chaotic crow search algorithm. In: Neural Computing and Applications, pp. 1–18. Springer, London (2017)
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Sayed, G.I., Soliman, M., Hassanien, A.E. (2018). Modified Optimal Foraging Algorithm for Parameters Optimization of Support Vector Machine. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_3
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DOI: https://doi.org/10.1007/978-3-319-74690-6_3
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