Advertisement

A New Multi-class Fuzzy Support Vector Machine Algorithm

  • Friedhelm Schwenker
  • Markus Frey
  • Michael Glodek
  • Markus Kächele
  • Sascha Meudt
  • Martin Schels
  • Miriam Schmidt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8774)

Abstract

In this paper a novel approach to fuzzy support vector machines (SVM) in multi-class classification problems is presented. The proposed algorithm has the property to benefit from fuzzy labeled data in the training phase and can determine fuzzy memberships for input data. The algorithm can be considered as an extension of the traditional multi-class SVM for crisp labeled data, and it also extents the fuzzy SVM approach for fuzzy labeled training data in the two-class classification setting. Its behavior is demonstrated on three benchmark data sets, the achieved results motivate the inclusion of fuzzy labeled data into the training set for various tasks in pattern recognition and machine learning, such as the design of aggregation rules in multiple classifier systems, or in partially supervised learning.

References

  1. 1.
    Abe, S.: Support Vector Machines for Pattern Classification (Advances in Pattern Recognition). Springer-Verlag New York, Inc., Secaucus (2005)Google Scholar
  2. 2.
    Bordes, A., Bottou, L., Gallinari, P., Weston, J.: Solving multiclass support vector machines with larank. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, pp. 89–96. ACM, New York (2007)Google Scholar
  3. 3.
    Chapelle, O., Schölkopf, B., Zien, A.: Semi-Supervised Learning, 1st edn. The MIT Press (2010)Google Scholar
  4. 4.
    Hady, M.F.A., Schwenker, F.: Semi-supervised learning. In: Bianchini, M., Maggini, M., Jain, L.C. (eds.) Handbook on Neural Information Processing. ISRL, vol. 49, pp. 215–239. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Transactions Neural Networks 13(2), 415–425 (2002)CrossRefGoogle Scholar
  6. 6.
    Kahsay, L., Schwenker, F., Palm, G.: Comparison of multiclass SVM decomposition schemes for visual object recognition. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 334–341. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems, Man and Cybernetics 4, 580–585 (1985)CrossRefGoogle Scholar
  8. 8.
    Lin, C.F., Wang, S.D.: Fuzzy support vector machines. IEEE Transactions on Neural Networks 13(2), 464–471 (2002)CrossRefGoogle Scholar
  9. 9.
    Platt, J.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers, pp. 61–74 (1999)Google Scholar
  10. 10.
    Scherer, S., Kane, J., Gobl, C., Schwenker, F.: Investigating fuzzy-input fuzzy-output support vector machines for robust voice quality classification. Computer Speech & Language 27(1), 263–287 (2013)CrossRefGoogle Scholar
  11. 11.
    Schwenker, F., Kestler, H.A., Palm, G.: Three learning phases for radial-basis-function networks. Neural Networks 14(4-5), 439–458 (2001)CrossRefGoogle Scholar
  12. 12.
    Thiel, C., Scherer, S., Schwenker, F.: Fuzzy-input fuzzy-output one-against-all support vector machines. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part III. LNCS (LNAI), vol. 4694, pp. 156–165. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Vapnik, V.: Statistical Learning Theory. John Wiley and Sons (1998)Google Scholar
  14. 14.
    Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms. Chapman Hall/CRC (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Friedhelm Schwenker
    • 1
  • Markus Frey
    • 1
  • Michael Glodek
    • 1
  • Markus Kächele
    • 1
  • Sascha Meudt
    • 1
  • Martin Schels
    • 1
  • Miriam Schmidt
    • 1
  1. 1.Institute of Neural Information ProcessingUlm UniversityUlmGermany

Personalised recommendations