Confusion Matrix Disagreement for Multiple Classifiers

  • Cinthia O. A. Freitas
  • João M. de Carvalho
  • JoséJosemar OliveiraJr.
  • Simone B. K. Aires
  • Robert Sabourin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

We present a methodology to analyze Multiple Classifiers Systems (MCS) performance, using the disagreement concept. The goal is to define an alternative approach to the conventional recognition rate criterion, which usually requires an exhaustive combination search. This approach defines a Distance-based Disagreement (DbD) measure using an Euclidean distance computed between confusion matrices and a soft-correlation rule to indicate the most likely candidates to the best classifiers ensemble. As case study, we apply this strategy to two different handwritten recognition systems. Experimental results indicate that the method proposed can be used as a low-cost alternative to conventional approaches.

Keywords

multiple classifiers systems pattern recognition classifiers diversity handwriting recognition 

References

  1. 1.
    Windeatt, T.: Diversity Measures for Multiple Classifier System Analysis and Design. Information Fusion 6(1), 21–36 (2005)CrossRefGoogle Scholar
  2. 2.
    Zouari, H.K.: Contribution à l’évaluation des méthodes de combinaison parallèle de classifieurs par simulation. Doctor Thesis, Université de Rouen (2004)Google Scholar
  3. 3.
    Duin, R.P.W., Pekalska, E., Tax, D.M.J.: The Characterization of Classification Problems by Classifier Disagreements. In: ICPR 2004, vol. 1, pp. 140–143 (2004)Google Scholar
  4. 4.
    Kuncheva, L.I., Whitaker, C.J.: Measures of Diversity in Classifier Ensembles. Machine Learning 51, 181–207 (2003)MATHCrossRefGoogle Scholar
  5. 5.
    Hadjitodorov, S.T., Kuncheva, L.I., Todorova, L.P.: Moderate Diversity for Better Cluster Ensembles (2005), http://www.informatics.bangor.ac.uk/kuncheva/-recent_publications.htm
  6. 6.
    Oh, I.-S., Suen, C.Y.: A Class-Modular Feedforward Neural Network for Handwriting Recognition. Pattern Recognition 35(1), 229–244 (2002)MATHCrossRefGoogle Scholar
  7. 7.
    Parker, J.R.: Algorithms for Image Processing and Computer Vision. Jonh Wiley & Sons, Chichester (1997)Google Scholar
  8. 8.
    Suen, C.Y., Guo, J., Li, Z.C.: Analysis and Recognition of Alphanumeric Handprints by Parts. IEEE Trans. on Systems, Man and Cybernetics 24(4), 614–631 (1994)CrossRefGoogle Scholar
  9. 9.
    Li, Z.C., Suen, C.Y., Guo, J.: A Regional Decomposition Method for Recognizing Handprinted Characters. IEEE Trans. on Systems, Man and Cybernetics 25(6), 998–1010 (1995)CrossRefGoogle Scholar
  10. 10.
    Freitas, C.O.A., Oliveira, L.E.S., Bortolozzi, F., Aires, S.B.K.: Handwritten Character Recognition Using Nonsymmetrical Perceptual Zoning. International Journal of Pattern Recognition and Artificial Intelligence, IJPRAI 21(1), 135–155 (2007)CrossRefGoogle Scholar
  11. 11.
    Rabiner, L., Juang, B.H.: Fundamental of Speech Recognition. Prentice-Hall, Englewood Cliffs (1993)Google Scholar
  12. 12.
    Oliveira, Jr.,J.J., Kapp, M.N., Freitas, C.O.A., Carvalho, J.M., Sabourin, R.: Handwritten month word recognition using multiple classifiers. In: XVII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI), vol. 1, pp. 82–89 (2004)Google Scholar
  13. 13.
    Freitas, C.O.A., Bortolozzi, F., Sabourin, R.: Study of Perceptual Similarity Between Differnet Lexicons. International Journal of Pattern Recognition and Artificial Intelligence, IJPRAI 18(7), 1321–1338 (2004)CrossRefGoogle Scholar
  14. 14.
    Viard-Gaudin, C.: The Ironoff User Manual. IRESTE, University of Nantes, France (1999)Google Scholar
  15. 15.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On Combining Classifiers. IEEE Trans. on PAMI 20(3), 226–239 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Cinthia O. A. Freitas
    • 1
  • João M. de Carvalho
    • 2
  • JoséJosemar OliveiraJr.
    • 2
  • Simone B. K. Aires
    • 3
  • Robert Sabourin
    • 4
  1. 1.Pontificia Universidade Católica do Paraná – PUCPRBrazil
  2. 2.Universidade Federal de Campina Grande – UFCGBrazil
  3. 3.Universidade Tecnológica Federal do Paraná – Campus Ponta Grossa – UTFP-PGBrazil
  4. 4.École de Technologie Supérieure - ETSCanada

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