Advertisement

Model Selection for Support Vector Machines Using Ant Colony Optimization in an Electronic Nose Application

  • Javier Acevedo
  • Saturnino Maldonado
  • Sergio Lafuente
  • Hilario Gomez
  • Pedro Gil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4150)

Abstract

Support vector machines, especially when using radial basis kernels, have given good results in the classification of different volatile compounds. We can achieve a feature extraction method adjusting the parameters of a modified radial basis kernel, giving more importance to those features that are important for classification proposes. However, the function that has to be minimized to find the best scaling factors is not derivable and has multiple local minima. In this work we propose to adapt the ideas of the ant colony optimization method to find an optimal value of the kernel parameters.

Keywords

Support Vector Machine Feature Extraction Method Electronic Nose Radial Basis Function Kernel Initial Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hines, E., Llobet, E., Gardner, J.: Electronic noses: a review of signal processing techniques. IEE Proc. Circuits Dev. and Systems 146, 297–310 (1999)CrossRefGoogle Scholar
  2. 2.
    Distante, C., Leo, M., Siciliano, P., Persaud, K.: On the study of feature extraction methods for an electronic nose. Sensors and Actuators B: Chem. 87, 274–288 (2002)CrossRefGoogle Scholar
  3. 3.
    Vapnik, N.V.: The Natureof Statistical Learning Theory. Springer, New York (2000) (1 edn., 1998)Google Scholar
  4. 4.
    Al-Khalifa, S., Maldonado, S., Gardner, J.: Identification of co and no2 using a thermally resistive microsensor and support vector machine. IEE Proc. Science Meas. and Tech. 150(6), 11–14 (2003)CrossRefGoogle Scholar
  5. 5.
    Pardo, M., Sberveglieri, G., Gardini, S., Dalcanale, E.: Classification of electronic nose data with support vector machines. Sensors and Actuators B: Chem. 107, 730–737 (2005)CrossRefGoogle Scholar
  6. 6.
    Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Machine Learning 46(1), 131–159 (2002)MATHCrossRefGoogle Scholar
  7. 7.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)MATHCrossRefGoogle Scholar
  8. 8.
    Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 11–32. McGraw-Hill, London (1999)Google Scholar
  9. 9.
    Platt, J.: Fast training of svms using sequential minimal optimization. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods – Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1998)Google Scholar
  10. 10.
    Joachims, T.: Estimating the generalization performance of a SVM efficiently. In: Langley, P. (ed.) Proc. of ICML 2000, pp. 431–438. Morgan Kaufmann, San Francisco (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Javier Acevedo
    • 1
  • Saturnino Maldonado
    • 1
  • Sergio Lafuente
    • 1
  • Hilario Gomez
    • 1
  • Pedro Gil
    • 1
  1. 1.University of Alcala, Teoría de la señalAlcala de HenaresSpain

Personalised recommendations