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Optimizing Support Vector Machine Parameters Using Bat Optimization Algorithm

  • Alaa Tharwat
  • Aboul Ella Hassanien
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 801)

Abstract

Support Vector Machine (SVM) parameters such as penalty parameter and kernel parameters have a great influence on the complexity and accuracy of predicting model. In this research, Bat algorithm (BA) has been employed to optimize the parameters of SVM, so that the classification performance can be increased. To evaluate the proposed model (BA-SVM), the experiment adopted different standard classification datasets which are obtained from the UCI machine learning data repository. The results of the BA-SVM algorithm are compared with grid search, which is a classical method of searching parameter values, and two other optimization algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results showed that the proposed model achieved competitive results and it can find the optimal values of SVM parameters.

Keywords

Optimization algorithms Support vector machine (SVM) Classification Parameter optimization Swarm intelligent Bat algorithm (BA) 

References

  1. 1.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. Wiley (2012)Google Scholar
  2. 2.
    Tharwat, A.: Linear vs. quadratic discriminant analysis classifier: a tutorial. Int. J. Appl. Pattern Recognit. 3(2) (2016) 145–180Google Scholar
  3. 3.
    Tharwat, A.: Principal component analysis-a tutorial. Int. J. Appl. Pattern Recognit. 3(3), 197–240 (2016)CrossRefGoogle Scholar
  4. 4.
    Yamany, W., Fawzy, M., Tharwat, A., Hassanien, A.E.: Moth-flame optimization for training multi-layer perceptrons. In: 11th International Computer Engineering Conference (ICENCO), pp. 267–272. IEEE (2015)Google Scholar
  5. 5.
    Yamany, W., Tharwat, A., Hassanin, M.F., Gaber, T., Hassanien, A.E., Kim, T.H.: A new multi-layer perceptrons trainer based on ant lion optimization algorithm. In: 2015 Fourth International Conference on Information Science and Industrial Applications (ISI), pp. 40–45. IEEE (2015)Google Scholar
  6. 6.
    Hosmer, D.W., Lemeshow, S.: Introduction to the logistic regression model, 2nd edn., pp. 1–30. In: Applied Logistic Regression (2000)Google Scholar
  7. 7.
    Tharwat, A., Gaber, T., Ibrahim, A., Hassanien, A.E.: Linear discriminant analysis: a detailed tutorial. AI Commun. (Preprint) 1–22 (2017)Google Scholar
  8. 8.
    Tharwat, A., Hassanien, A.E., Elnaghi, B.E.: A BA-based algorithm for parameter optimization of support vector machine. Pattern Recognit. Lett. (2016)Google Scholar
  9. 9.
    Drucker, H., Wu, D., Vapnik, V.N.: Support vector machines for spam categorization. IEEE Trans. Neural Netw. 10(5), 1048–1054 (1999)CrossRefGoogle Scholar
  10. 10.
    Doucet, J.P., Barbault, F., Xia, H., Panaye, A., Fan, B.: Nonlinear SVM approaches to QSPR/QSAR studies and drug design. Current Comput. Aided Drug Des. 3(4), 263–289 (2007)CrossRefGoogle Scholar
  11. 11.
    Tharwat, A., Moemen, Y.S., Hassanien, A.E.: A predictive model for toxicity effects assessment of biotransformed hepatic drugs using iterative sampling method. Sci. Rep. 6 (2016)Google Scholar
  12. 12.
    Vatsa, M., Singh, R., Noore, A.: Improving biometric recognition accuracy and robustness using DWT and SVM watermarking. IEICE Electron. Express 2(12), 362–367 (2005)CrossRefGoogle Scholar
  13. 13.
    Gaber, T., Tharwat, A., Hassanien, A.E., Snasel, V.: Biometric cattle identification approach based on weber’s local descriptor and adaboost classifier. Comput. Electron. Agric. 122, 55–66 (2016)CrossRefGoogle Scholar
  14. 14.
    Tharwat, A., Gaber, T., Hassanien, A.E.: Two biometric approaches for cattle identification based on features and classifiers fusion. Int. J. Image Min. 1(4), 342–365 (2015)CrossRefGoogle Scholar
  15. 15.
    Byvatov, E., Schneider, G.: Support vector machine applications in bioinformatics. Appl. Bioinf. 2(2), 67–77 (2002)Google Scholar
  16. 16.
    Semary, N.A., Tharwat, A., Elhariri, E., Hassanien, A.E.: Fruit-based tomato grading system using features fusion and support vector machine. In: Intelligent Systems’2014, pp. 401–410. Springer (2015)Google Scholar
  17. 17.
    Tharwat, A., Gaber, T., Hassanien, A.E.: One-dimensional vs. two-dimensional based features: plant identification approach. J. Appl. Logic (2016)Google Scholar
  18. 18.
    Lin, S.W., Ying, K.C., Chen, S.C., Lee, Z.J.: Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst. Appl. 35(4), 1817–1824 (2008)CrossRefGoogle Scholar
  19. 19.
    Zhang, X., Chen, X., He, Z.: An ACO-based algorithm for parameter optimization of support vector machines. Expert Syst. Appl. 37(9), 6618–6628 (2010)CrossRefGoogle Scholar
  20. 20.
    Tharwat, A., Elnaghi, B.E., Hassanien, A.E.: Meta-heuristic algorithm inspired by grey wolves for solving function optimization problems. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 480–490. Springer (2016)Google Scholar
  21. 21.
    Elhoseny, M., Tharwat, A., Hassanien, A.E.: Bezier curve based path planning in a dynamic field using modified genetic algorithm. J. Comput. Sci. (2017)Google Scholar
  22. 22.
    Tharwat, A., Gaber, T., Hassanien, A.E., Elnaghi, B.E.: Particle swarm optimization: a tutorial. In: Handbook of Research on Machine Learning Innovations and Trends, pp. 614–635. IGI Global (2017)Google Scholar
  23. 23.
    Tharwat, A., Houssein, E.H., Ahmed, M.M., Hassanien, A.E., Gabel, T.: Mogoa algorithm for constrained and unconstrained multi-objective optimization problems. Appl. Intell. 1–16 (2017)Google Scholar
  24. 24.
    Elhoseny, M., Tharwat, A., Yuan, X., Hassanien, A.E.: Optimizing K-coverage of mobile WSNs. Expert Syst. Appl. 92, 142–153 (2018)CrossRefGoogle Scholar
  25. 25.
    Elhoseny, M., Tharwat, A., Farouk, A., Hassanien, A.E.: K-coverage model based on genetic algorithm to extend WSN lifetime. IEEE Sens. Lett. 1(4), 1–4 (2017)CrossRefGoogle Scholar
  26. 26.
    Yang, X.S.: Nature-inspired optimization algorithms, 1st edn. Elsevier (2014)Google Scholar
  27. 27.
    Wang, G., Guo, L.: A novel hybrid bat algorithm with harmony search for global numerical optimization. J. Appl. Math. 2013 (2013)Google Scholar
  28. 28.
    Nakamura, R.Y., Pereira, L.A., Costa, K., Rodrigues, D., Papa, J.P., Yang, X.S.: BBA: a binary bat algorithm for feature selection. In: 25th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 291–297. IEEE (2012)Google Scholar
  29. 29.
    Friedrichs, F., Igel, C.: Evolutionary tuning of multiple SVM parameters. Neurocomputing 64, 107–117 (2005)CrossRefGoogle Scholar
  30. 30.
    Tharwat, A., Hassanien, A.E.: Chaotic antlion algorithm for parameter optimization of support vector machine. Appl. Intell. 1–17 (2017)Google Scholar
  31. 31.
    Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Mach. Learn. 46(1–3), 131–159 (2002)CrossRefGoogle Scholar
  32. 32.
    Wang, L.: Support Vector Machines: Theory and Applications, vol. 177. Springer Science & Business Media (2005)Google Scholar
  33. 33.
    Ali, S., Smith, K.: Automatic parameter selection for polynomial kernel. In: Proceedings of IEEE International Conference on Information Reuse and Integration, (IRI 2003), Lens, France, 27–29 October, pp. 243–249. IEEE (2003)Google Scholar
  34. 34.
    Wu, C.H., Tzeng, G.H., Lin, R.H.: A novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Syst. Appl. 36(3), 4725–4735 (2009)CrossRefGoogle Scholar
  35. 35.
    Subasi, A.: Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput. Biol. Med. 43(5), 576–586 (2013)CrossRefGoogle Scholar
  36. 36.
    Tharwat, A., Moemen, Y.S., Hassanien, A.E.: Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines. J. Biomed. Inf. 68, 132–149 (2017)CrossRefGoogle Scholar
  37. 37.
    Scholköpf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press (2001)Google Scholar
  38. 38.
    Tharwat, A., Zawbaa, H.M., Gaber, T., Hassanien, A.E., Snasel, V.: Automated zebrafish-based toxicity test using bat optimization and adaboost classifier. In: Proceedings of the 11th International Computer Engineering Conference (ICENCO), pp. 169–174. IEEE (2015)Google Scholar
  39. 39.
    Zhao, M., Fu, C., Ji, L., Tang, K., Zhou, M.: Feature selection and parameter optimization for support vector machines: a new approach based on genetic algorithm with feature chromosomes. Expert Syst. Appl. 38(5), 5197–5204 (2011)CrossRefGoogle Scholar
  40. 40.
    Keerthi, S.S., Lin, C.J.: Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput. 15(7), 1667–1689 (2003)CrossRefGoogle Scholar
  41. 41.
    Kecman, V.: Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models. MIT Press (2001)Google Scholar
  42. 42.
    Blake, C., Merz, C.J.: UCI repository of machine learning databases (1998)Google Scholar
  43. 43.
    Huang, C.L., Wang, C.J.: A GA-based feature selection and parameters optimization for support vector machines. Expert Syst. Appl. 31(2), 231–240 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of EngineeringSuez Canal UniversityIsmailiaEgypt
  2. 2.Faculty of Computer Science and EngineeringFrankfurt University of Applied SciencesFrankfurtGermany
  3. 3.Faculty of Computers and InformationCairo UniversityGizaEgypt
  4. 4.Scientific Research Group in Egypt (SRGE)GizaEgypt

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