Advertisement

Performance Analysis and Comparison of Linear Combiners for Classifier Fusion

  • Giorgio Fumera
  • Fabio Roli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)

Abstract

In this paper, we report a theoretical and experimental comparison between two widely used combination rules for classifier fusion: simple average and weighted average of classifiers outputs. We analyse the conditions which affect the difference between the performance of simple and weighted averaging and discuss the relation between these conditions and the concept of classifiers’ “imbalance”. Experiments aimed at assessing some of the theoretical results for cases where the theoretical assumptions could not be hold are reported.

Keywords

Optimal Weight Decision Boundary Simple Average Individual Classifier Classifier Ensemble 
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.

References

  1. 1.
    Lam, L., Suen, C. Y.: Application of Majority Voting to Pattern Recognition: An Analysis of Its Behavior and Performance. IEEE Trans, on Systems, Man and Cybernetics-Part A27 (1997) 553–568Google Scholar
  2. 2.
    Turner, K., Ghosh, J.: Analysis of Decision Boundaries in Linearly Combined Neural Classifiers. Pattern Recognition 29 (1996) 341–348CrossRefGoogle Scholar
  3. 3.
    Turner, K., Ghosh, J.: Linear and Order Statistics Combiners for Pattern Classification. In: Sharkey, A. J. C. (ed.): Combining Artificial Neural Nets. Springer (1999) 127–161Google Scholar
  4. 4.
    Xu, L., Krzyzak, A., Suen, C. Y.: Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition. IEEE Trans, on Systems, Man, and Cybernetics 22 (1992) 418–435CrossRefGoogle Scholar
  5. 5.
    Huang, Y. S., Suen, C. Y.: A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals. IEEE Transactions on Pattern Analysis and Machine Intelligence 17 (1995) 90–94CrossRefGoogle Scholar
  6. 6.
    Ueda, N.: Optimal Linear Combination of Neural Networks for Improving Classification Performance. IEEE Trans, on Pattern Analisys and Machine Int. 22(2000)207–215CrossRefGoogle Scholar
  7. 7.
    Turner, K.: Linear and Order Statistics Combiners for Reliable Pattern Classification. PhD thesis. The University of Texas, Austin, TX (1996)Google Scholar
  8. 8.
    Perrone, M., Cooper, L. N.: When Networks Disagree: Ensemble Methods for Hybrid Neural Networks. In: Mammone, R.J. (ed.): Neural Networks for Speech and Image Processing. Chapman-Hall, New York (1993)Google Scholar
  9. 9.
    Roli, F.: Multisensor Image Recognition by Neural Networks with Understandable Behaviour. Int. J. of Pattern Recognition and Artificial Intelligence 10 (1996) 887–917CrossRefGoogle Scholar
  10. 10.
    Kittler, J., Roli, F. (eds.): Proc. of the 1st and 2nd Int. Workshop on Multiple Classifier Systems. Springer-Verlag, LNCS, Vol. 1857 (2000), and Vol. 2096 (2001)Google Scholar
  11. 11.
    Turner, K., Ghosh, J.: Robust Combining of Disparate Classifiers through Order Statistics. To appear in: Pattern Analysis and Applications, special issue on “Fusion of Multiple Classifiers”Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Giorgio Fumera
    • 1
  • Fabio Roli
    • 1
  1. 1.Dept. of Electrical and Electronic Eng.University of CagliariCagliariItaly

Personalised recommendations