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Selection of Classifiers Based on Multiple Classifier Behaviour

  • Giorgio Giacinto
  • Fabio Roli
  • Giorgio Fumera
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)

Abstract

In the field of pattern recognition, the concept of Multiple Classifier Systems (MCSs) was proposed as a method for the development of high performance classification systems. At present, the common “operation” mechanism of MCSs is the “combination” of classifiers outputs. Recently, some researchers pointed out the potentialities of “dynamic classifier selection” (DCS) as a new operation mechanism. In this paper, a DCS algorithm based on the MCS behaviour is presented. The proposed method is aimed to exploit the behaviour of the MCS in order to select, for each test pattern, the classifier that is more likely to provide the correct classification. Reported results on the classification of different data sets show that dynamic classifier selection based on MCS behaviour is an effective operation mechanism for MCSs.

Keywords

Multiple Classifier Systems Combination of Classifiers Dynamic Classifier Selection Image Classification 

References

  1. 1.
    Xu, L., Krzyzak, A., and Suen, C.Y.: Methods for combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. on Systems, Man, and Cyb., 22(3) (1992) 418–435CrossRefGoogle Scholar
  2. 2.
    Huang, Y.S. and Suen, C.Y.: A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Trans. On Pattern Analysis and Machine Intelligence 17(1) (1995) 90–94CrossRefGoogle Scholar
  3. 3.
    Giacinto, G., and Roli F.: Adaptive Selection Of Image Classifiers. Proc. of the 9th International Conference on Image Analysis and Processing, Lecture Notes in Computer Science 1310, Springer Verlag Ed. (1997) 38–45Google Scholar
  4. 4.
    Woods, K., Kegelmeyer, W.P., and Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(4) (1997) 405–410CrossRefGoogle Scholar
  5. 5.
    Giacinto G. and Roli F.: Methods for Dynamic Classifier Selection. 10th International Conference on Image Analysis and Processing, Venice, Italy (1999), 659–664Google Scholar
  6. 6.
    Quinlan, J.R.: C4.5 Programs for Machine Learning. Morgan Kaufmann, (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

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

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