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European Conference on the Applications of Evolutionary Computation

EvoApplications 2014: Applications of Evolutionary Computation pp 514–525Cite as

Adaptive Genetic Algorithm to Select Training Data for Support Vector Machines

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8602))

Abstract

This paper presents a new adaptive genetic algorithm (AGA) to select training data for support vector machines (SVMs). SVM training data selection strongly influences the classification accuracy and time, especially in the case of large and noisy data sets. In the proposed AGA, a population of solutions evolves with time. The AGA parameters, including the chromosome length, are adapted according to the current state of exploring the solution space. We propose a new multi-parent crossover operator for an efficient search. A new metric of distance between individuals is introduced and applied in the AGA. It is based on the fast analysis of the vectors distribution in the feature space obtained using principal component analysis. An extensive experimental study performed on the well-known benchmark sets along with the real-world and artificial data sets, confirms that the AGA outperforms a standard GA in terms of the convergence capabilities. Also, it reduces the number of support vectors and allows for faster SVM classification.

This work has been supported by the Polish Ministry of Science and Higher Education under research grant no. IP2012 026372 from the Science Budget 2013–2015.

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References

  1. Balcázar, J., Dai, Y., Watanabe, O.: A Random Sampling Technique for Training Support Vector Machines. In: Abe, N., Khardon, R., Zeugmann, T. (eds.) ALT 2001. LNCS (LNAI), vol. 2225, pp. 119–134. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  2. Chang, C.C., Pao, H.K., Lee, Y.J.: RSVM based two-teachers-one-student semi-supervised learning algorithm. Neural Networks 25, 57–69 (2012)

    Article  Google Scholar 

  3. Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Trans. on Intell. Systems and Technology 2, 27:1–27:27 (2011)

    Google Scholar 

  4. Chien, L.J., Chang, C.C., Lee, Y.J.: Variant methods of reduced set selection for reduced support vector machines. J. Inf. Sci. Eng. 26(1), 183–196 (2010)

    MATH  Google Scholar 

  5. Corne, D., Dorigo, M., Glover, F., Dasgupta, D., Moscato, P., Poli, R., Price, K.V.: New ideas in optimization, pp. 219–234. McGraw-Hill Ltd. (1999)

    Google Scholar 

  6. Cortes, C., Vapnik, V.: Support-Vector Networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  7. Elamin, E.E.A.: A proposed genetic algorithm selection method. In: 1st National Symposium (NITS), pp. 1–8 (2006)

    Google Scholar 

  8. Kawulok, M., Nalepa, J.: Support vector machines training data selection using a genetic algorithm. In: Hancock, E. Imiya, A., Kuijper, A. Kudo, M., Omachi, S., Windeatt, T., Yamada, K.: (eds.): SSPR & SPR 2012, LNCS 7626, pp. 557-565. Springer, Heidelberg (2012)

    Google Scholar 

  9. Koggalage, R., Halgamuge, S.: Reducing the number of training samples for fast support vector machine classification. Neural Inf. Process. Lett. and Reviews 2(3), 57–65 (2004)

    Google Scholar 

  10. Lee, Y.J., Huang, S.Y.: Reduced support vector machines: A statistical theory. IEEE Trans. on Neural Networks 18(1), 1–13 (2007)

    Article  Google Scholar 

  11. Musicant, D.R., Feinberg, A.: Active set support vector regression. IEEE Trans. on Neural Networks 15(2), 268–275 (2004)

    Article  Google Scholar 

  12. Phung, S.L., Chai, D., Bouzerdoum, A.: Adaptive skin segmentation in color images. In: IEEE Int. Conf. on Acoustics, Speech and Signal Proc., pp. 353–356 (2003)

    Google Scholar 

  13. Schohn, G., Cohn, D.: Less is more: Active learning with support vector machines. In: 17th Int. Conf. on Mach. Learn., pp. 839–846. Morgan Kaufmann Inc. (2000)

    Google Scholar 

  14. Shin, H., Cho, S.: Neighborhood property-based pattern selection for support vector machines. Neural Comput. 19(3), 816–855 (2007)

    Article  MATH  Google Scholar 

  15. Tsang, I.W., Kwok, J.T., Cheung, P.M.: Core vector machines: Fast SVM training on very large data sets. J. of Machine Learn. Res. 6, 363–392 (2005)

    MATH  MathSciNet  Google Scholar 

  16. Wang, D., Shi, L.: Selecting valuable training samples for SVMs via data structure analysis. Neurocomputing 71, 2772–2781 (2008)

    Article  Google Scholar 

  17. Wang, J., Neskovic, P., Cooper, L.N.: Training Data Selection for Support Vector Machines. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 554–564. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  18. Zeng, Z.Q., Xu, H.R., Xie, Y.Q., Gao, J.: A geometric approach to train SVM on very large data sets. Intell. Sys. and Knowl. Eng. 1, 991–996 (2008)

    Google Scholar 

  19. Zhang, W., King, I.: Locating support vectors via \(\beta \)-skeleton technique. In: Int. Conf. on Neural Inf. Process., 1423–1427 (2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Silesian University of Technology, Gliwice, Poland

    Jakub Nalepa & Michal Kawulok

Authors
  1. Jakub Nalepa
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  2. Michal Kawulok
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Corresponding author

Correspondence to Jakub Nalepa .

Editor information

Editors and Affiliations

  1. Depto. Estadística e Investigación, Universidad Politécnica de Valencia, Valencia, Spain

    Anna I. Esparcia-Alcázar

  2. University of Granada, Granada, Spain

    Antonio M. Mora

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Nalepa, J., Kawulok, M. (2014). Adaptive Genetic Algorithm to Select Training Data for Support Vector Machines. In: Esparcia-Alcázar, A., Mora, A. (eds) Applications of Evolutionary Computation. EvoApplications 2014. Lecture Notes in Computer Science(), vol 8602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45523-4_42

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  • DOI: https://doi.org/10.1007/978-3-662-45523-4_42

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45522-7

  • Online ISBN: 978-3-662-45523-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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