Skip to main content
SpringerLink
Log in

Classifier Combinations: Implementations and Theoretical Issues

  • Conference paper
  • First Online:
Multiple Classifier Systems (MCS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1857))

Included in the following conference series:

Abstract

Much work has been done in the past decade to combine decisions of multiple classifiers in order to obtain improved recognition results. Many methodologies have been designed and implemented for this purpose. This article considers some of the current developments according to the structure of the combination process, and discusses some issues involved in each structure. In addition, theoretical investigations that have been performed in this area are also examined, and some related issues are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A. S. Atukorale and P. N. Suganthan. Combining classifiers based on confidence values. In Proc. Fifth Int. Conf. on Document Analysis and Recognition, pages 37–40, Bangalore, India, Sept. 1999.

    Google Scholar 

  2. Y.-C. Chim, A. A. Kassim, and Y. Ibrahim. Dual classifier system for handprinted alphanumeric character recognition. Pattern Analysis and Applications, 1:155–162, 1998.

    Article  MATH  Google Scholar 

  3. N. C. de Condorcet. Essai sur l’application de l’analyse’ a la probabilité des décisions rendues á la pluralité des voix Imprimerie Royale, Paris, 1785.

    Google Scholar 

  4. P. D. Gader, D. Hepp, B. Forrester, and T. Peurach. Pipelined systems for recognition of handwritten digits in USPS ZIP codes. In Proc. U. S. Postal Service Advanced Technology Conf, pages 539–548, 1990.

    Google Scholar 

  5. D. Guillevic and C. Y. Suen. HMM-KNN word recognition engine for bank cheque processing. In Proc. 14th ICPR, pages II: 1526–1529, Brisbane, Australia, August 1998.

    Google Scholar 

  6. T. K. Ho. The random subspace method for constructing decision forests. IEEE Trans. PAMI, 20(8):832–844, 1998.

    Google Scholar 

  7. T. K. Ho, J. J. Hull, and S. N. Srihari. Decision combination in multiple classifier systems. IEEE Trans. PAMI, 16:66–75, 1994.

    Google Scholar 

  8. T.K. Ho. Adaptive coordination of multiple classifiers. In J.J. Hull and S.L. Taylor, editors, Document Analysis Systems II, pages 371–384. World Scientific, 1998.

    Google Scholar 

  9. G.F. Houle, D.B. Aragon, R.W. Smith, M. Shridhar, and D. Kimura. A multilayered corroboration-based check reader. In J.J. Hull and S.L. Taylor, editors, Document Analysis Systems II, pages 495–546. World Scientific, 1998.

    Google Scholar 

  10. Y. S. Huang and C. Y. Suen. Combination of multiple experts for the recognition of unconstrained handwritten numerals. IEEE Trans. Pattern Anal. Mach. Intell, 17:90–94, 1995.

    Article  Google Scholar 

  11. C. Ji and S. Ma. Combinations of weak classifiers. IEEE Trans. Neural Networks, 8(1):32–42, 1997.

    Article  Google Scholar 

  12. J. Kittler. Combining classifiers: a theoretical framework. Pattern Analysis and Applications, 1:18–27, 1998.

    Article  Google Scholar 

  13. J. Kittler. On combining classifiers. IEEE Trans. PAMI, 20(3):226–239, 1998.

    Google Scholar 

  14. J. Kittler. Pattern classification: fusion of information. In Proc. Int. Conf. on Advances in Pattern Recognition, pages 13–22, Plymouth, UK, November 1998.

    Google Scholar 

  15. J. Kittler, M. Hatef, and R.P.W. Duin. Combining classifiers. In Proc. 13th Int. Conf. on Pattern Recognition, pages II: 897–901, Vienna, Austria, August 1996.

    Google Scholar 

  16. E. M. Kleinberg. An overtraining-resistant stochastic modeling method for pattern recognition. Annals of Statistics, 24(6):2319–2349, 1996.

    Article  MATH  MathSciNet  Google Scholar 

  17. E. M. Kleinberg and T. K. Ho. Pattern recognition by stochastic modelling. In Pre-Proc. 3rd Int. Workshop on Frontiers in Handwriting Recognition, pages 175–183, Buffalo, NY, USA, May 1993.

    Google Scholar 

  18. S. Knerr, O. Baret, D. Price, J.C. Simon, V. Anisimov, and N. Gorski. The A2iA recognition system for handwritten checks. In Proc. IAPR Workshop on Document Analysis Systems, pages 431–494, Malvern, Penn., USA, October 1996.

    Google Scholar 

  19. L. Lam and C. Y. Suen. Structural classification and relaxation matching of totally unconstrained handwritten zip-code numbers. Pattern Recognition, 21(1):19–31, 1988.

    Article  Google Scholar 

  20. L. Lam and C. Y. Suen. Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Trans. Systems, Man, and Cybernetics, 27(5):553–568, 1997.

    Article  Google Scholar 

  21. L. Lam and C.Y. Suen. Optimal combinations of pattern classifiers. Pattern Recognition Letters, 16:945–954, 1995.

    Article  Google Scholar 

  22. D.S. Lee and S.N. Srihari. Handprinted digit recognition: A comparison of algorithms. In Pre-Proc. 3rd Int. Workshop on Frontiers in Handwriting Recognition, pages 153–162, Buffalo, USA, May1993.

    Google Scholar 

  23. R.K. Powalka, N. Sherkat, and R.J. Whitrow. Multiple recognizer combination topologies. In M.L. Simner, C.G. Leedham, and A.J.W.M. Thomasson, editors, Handwriting and Drawing Research: Basic and Applied Issues, pages 329–342. IOS Press, 1996.

    Google Scholar 

  24. A.F.R. Rahman and M.C. Fairhurst. An evaluation of multi-expert configurations for the recognition of handwritten numerals. Pattern Recognition, 31(9):1255–1273, 1998.

    Article  Google Scholar 

  25. F.R. Rahman and M. C. Fairhurst. Serial combination of multiple experts: a unified evaluation. Pattern Analysis and Applications, 2:292–311, 1999.

    Article  Google Scholar 

  26. N.V.S. Reddy and P. Nagabhushan. A connectionist expert system model for conflict resolution in unconstrained handwritten recognition. Pattern Recognition Letters, 19:161–169, 1998.

    Article  MATH  Google Scholar 

  27. N. W. Strathy. Handwriting recognition for cheque processing. In Proc. 2nd Int. Conf. on Multimodal Interface, pagesIII:47–50, Hong Kong, Jan. 1999.

    Google Scholar 

  28. C.Y. Suen, C. Nadal, R. Legault, T. A. Mai, and L. Lam. Computer recognition of unconstrained handwritten numerals. Proc. IEEE, 80:1162–1180, 1992.

    Article  Google Scholar 

  29. D. Wang, J. M. Keller, C.A. Carson, K.K. McAdoo-Edwards, and C. W. Bailey. Use of fuzzy-logic-inspired features to improve bacterial recognition through classifier fusion. IEEE Trans. SMC, 28B(4):583–591, 1998.

    Google Scholar 

  30. L. Xu, A. Krzyzak, and C. Y. Suen. Methods of combining multiple classifiers and their application to handwritten numeral recognition. IEEE Trans. Systems, Man, and Cybernetics, 22:418–435, 1992.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, Hong Kong Institute of Education, 10 Lo Ping Road, Tai Po, Hong Kong

    Louisa Lam

  2. Centre for Pattern Recognition and Machine Intelligence, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec, H3G 1M8, Canada

    Louisa Lam

Authors
  1. Louisa Lam
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lam, L. (2000). Classifier Combinations: Implementations and Theoretical Issues. In: Multiple Classifier Systems. MCS 2000. Lecture Notes in Computer Science, vol 1857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45014-9_7

Download citation

  • DOI: https://doi.org/10.1007/3-540-45014-9_7

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67704-8

  • Online ISBN: 978-3-540-45014-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation