Abstract
Support vector machines are widely used as superior classifiers for many different applications. Accuracy of the constructed support vector machine classifier depends on the proper parameter tuning. One of the most common used techniques for parameter determination is grid search. This optimization can be done more precisely and computationally more efficiently by using stochastic search metaheuristics. In this paper we propose using enhanced fireworks algorithm for support vector machine parameter optimization. We tested our approach on standard benchmark datasets from the UCI Machine Learning Repository and compared the results with grid search and with results obtained by other swarm intelligence approaches from the literature. Enhanced fireworks algorithm proved to be very successful, but most importantly it significantly outperformed other algorithms for more realistic cases for which there were separate test sets, rather than doing only cross validation.
M. Tuba–This research is supported by Ministry of Education, Science and Technogical Development of Republic of Serbia, Grant No. III-44006.
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References
Xian, G.: An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM. Expert Syst. Appl. 37, 6737–6741 (2010)
Liu, H., Liu, L., Zhang, H.: Ensemble gene selection for cancer classification. Pattern Recogn. 43, 2763–2772 (2010)
Gumus, E., Kilic, N., Sertbas, A., Ucan, O.N.: Evaluation of face recognition techniques using PCA, wavelets and SVM. Expert Syst. Appl. 37, 6404–6408 (2010)
Malon, C., Uchida, S., Suzuki, M.: Mathematical symbol recognition with support vector machines. Pattern Recogn. Lett. 29, 1326–1332 (2008)
Pai, P.F., Hsu, M.F., Lin, L.: Enhancing decisions with life cycle analysis for risk management. Neural Comput. Appl. 24, 1717–1724 (2014)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)
Larroza, A., Moratal, D., Paredes-Sanchez, A., Soria-Olivas, E., Chust, M.L., Arribas, L.A., Arana, E.: Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI. J. Magn. Reson. Imaging 42, 1362–1368 (2015)
Subasi, A., Gursoy, M.I.: EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl. 37, 8659–8666 (2010)
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, 1817–1824 (2008)
Bao, Y., Hu, Z., Xiong, T.: A PSO and pattern search based memetic algorithm for SVMs parameters optimization. Neurocomputing 117, 98–106 (2013)
Wu, Q.: A self-adaptive embedded chaotic particle swarm optimization for parameters selection of Wv-SVM. Expert Syst. Appl. 38, 184–192 (2011)
Liu, F., Zhou, Z.: A new data classification method based on chaotic particle swarm optimization and least square-support vector machine. Chemometr. Intell. Lab. Syst. 147, 147–156 (2015)
Mustaffa, Z., Yusof, Y., Kamaruddin, S.S.: Enhanced artificial bee colony for training least squares support vector machines in commodity price forecasting. J. Comput. Sci. 5, 196–205 (2014)
Lichman, M.: UCI machine learning repository (2013)
Chen, H.J., Yang, B., Wang, S.J., Wang, G., Liu, D.Y., Li, H.Z., Liu, W.B.: Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy. Appl. Math. Comput. 239, 180–197 (2014)
Wang, L., Chu, F., Jin, G.: Cancer diagnosis and protein secondary structure prediction using support vector machines. In: Wang, L. (ed.) Support Vector Machines: Theory and Applications, pp. 343–364. Springer-Verlag, Berlin Heidelberg (2005)
Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010, Part I. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010)
Zheng, S., Janecek, A., Tan, Y.: Enhanced fireworks algorithm. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2069–2077 (2013)
Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Technical report, National Taiwan University (2010)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)
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Tuba, E., Tuba, M., Beko, M. (2016). Support Vector Machine Parameters Optimization by Enhanced Fireworks Algorithm. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_52
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