On the Optimization of Multiclass Support Vector Machines Dedicated to Speech Recognition

  • Freha Mezzoudj
  • Assia Benyettou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)


We present in this paper an interesting approach to enhance the performance of multi-classification using Genetic Algorithm. Two systems for an instance selection and feature selection are respectively introduced. We combined Genetic Algorithm with multiclass Support Vector Machines in order to reduce the learning set. The goal is to simplify the learning process and to improve the generalization. The results obtained on speech corpus show encouraging improvements in terms of processing time and classification accuracies.


Support Vector Machines Genetic Algorithms Multi-classification Speech recognition Machine learning Feature Selection Instance Selection 


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  1. 1.
    Vapnik, V.: The Nature of Statistical Learning Theory, Berlin, Germany (1995)Google Scholar
  2. 2.
    Wang, L.: Support Vector Machines: Theory and Applications. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  3. 3.
    Zribi, B.S., Ben Ayed, M.D., Ellouze, N.: Support Vector Machines Approaches and Its Application to Speaker Identification. In: IEEE International Conference on Digital Eco-Systems and Technologies, DEST 2009, Turkey, pp. 662–667 (2009)Google Scholar
  4. 4.
    Tang, B., Mazzoni, D.: Multiclass Reduced-Set Support Vector Machines. In: 23rd International Conference on ML, USA (2006)Google Scholar
  5. 5.
    Ahn, H., Kim, K.: Bankruptcy Prediction Modeling with Hybrid Case-Based Reasoning and Genetic Algorithms Approach. Appl. Soft Comput. 9, 599–607 (2009)CrossRefGoogle Scholar
  6. 6.
    Cherkassky, V., Mulier, F.: Learning from Data: Concepts, Theory and Methods, 2nd edn. IEEE Press (2007)Google Scholar
  7. 7.
    Crammer, K., Singer, Y.: On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines. J. Mach. Learn. Research 2, 265–292 (2001)Google Scholar
  8. 8.
    Joachims, T.: SVMmulticlass V2.12,
  9. 9.
    Huang, Z., Chen, H., Hsu, C.J., Chen, W.H., Wu, S.: Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study. Decis. Support Syst. 37, 543–558 (2004)CrossRefGoogle Scholar
  10. 10.
    Guyon, I., Elissee, A.: An Introduction to Variable and Feature Selection. J. Mach. Learn. Research 3, 1157–1182 (2003)zbMATHGoogle Scholar
  11. 11.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Massachusetts (1989)zbMATHGoogle Scholar
  12. 12.
    Steeb, W.H., Hardy, Y., Stoop, R.: Genetic Algorithms in The Nonlinear Workbook, 3rd edn. World Scientific Publishing (2005)Google Scholar
  13. 13.
    Boehm, O., Hardoon, D.R., Manevitz, L.M.: Classifying Cognitive States of Brain Activity via One Class Neural Networks with Feature Selection by Genetic Algorithms. Int. J. Mach. Learn. Cybern. 2(3), 125–134 (2011)CrossRefGoogle Scholar
  14. 14.
    Nair, S.S.K., Subba, R.N.V., Hareesha, K.S.: An Evaluation of Feature Selection Approaches in Finding Amyloidogenic Regions in Protein Sequences. Int. J. Comput. Appl. 8(2) (2010)Google Scholar
  15. 15.
    HTK: Hidden Markov Model Toolkit: Speech Recognition Research Toolkit,

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Freha Mezzoudj
    • 1
  • Assia Benyettou
    • 1
  1. 1.Laboratory Signal-IMage-PArole (SIMPA), Department of Computer ScienceUniversity of Science and Technology of Oran USTOAlgeria

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