Skip to main content
SpringerLink
Log in

The Practical Performance Characteristics of Tomographically Filtered Multiple Classifier Fusion

  • Conference paper
  • First Online:
Multiple Classifier Systems (MCS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2709))

Included in the following conference series:

  • 837 Accesses

Abstract

In this paper we set out to give an indication both of the classification performance and the robustness to estimation error of the authors’ ‘tomographic’ classifier fusion methodology in a comparative field test with the sum and product classier fusion methodologies.

In encompassing this, we find evidence to confirm that the tomographic methodology represents a generally superior fusion strategy across the entire range of problem dimensionalities, final results indicating an as much as 25% improvement on the next nearest performing combination scheme at the extremity of the tested range.

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. D. Windridge, J. Kittler, “A Morphologically Optimal Strategy for Classifier Combination: Multiple Expert Fusion as a Tomographic Process”, IEEE PAMI, Vol 25, no. 3, March 2003

    Google Scholar 

  2. D. Windridge, J. Kittler, “Classifier Combination as a Tomographic Process”, Multiple Classifier Systems, LNCS. Vol. 2096, 2001.

    Google Scholar 

  3. D. Windridge, J. Kittler, “A Generalised Solution to the Problem of Multiple Expert Fusion”, Univ. of Surrey Technical Report: VSSP-TR-5/2000)

    Google Scholar 

  4. D. Windridge, J. Kittler, “An Efficient Algorithmic Implementation of Tomographic Classifier Combination”, PR journal, Submitted

    Google Scholar 

  5. D. Windridge, J. Kittler, “Morphologically Unbiased Classifier Combination Through Graphical PDF Correlation”, LNCS 2396, August 2002

    Google Scholar 

  6. D. Windridge, J. Kittler, “On the General Application of the Tomographic Classifier Fusion Methodology, LNCS. Vol. 2364, June 2002

    Google Scholar 

  7. Windridge D., Kittler J., “Combined Classifier Optimisation via Feature Selection”, Proceedings “Advances in Pattern Recognition”, Lecture Notes in Computer Science. Vol. 1876

    Google Scholar 

  8. K. M. Ali and M.J. Pazzani, “On the link between error correlation and error reduction in decision tree ensembles”, Technical Report 95-38, ICS-UCI, 1995

    Google Scholar 

  9. T.K. Ho, J.J. Hull, and S.N. Srihari, “Decision combination in multiple classifier systems”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 1, 1994, 66–75.

    Article  Google Scholar 

  10. L. Xu, A. Krzyzak and C.Y. Suen, “Methods of combining multiple classifiers and their application to handwriting recognition”, IEEE Trans. SMC, vol. 22, no. 11, 1994, 1539–1549

    Google Scholar 

  11. Neal R., Probabilistic inference using Markov chain Monte Carlo methods. Tech. rep. CRG-TR-93-1 1993, Dept. of Computer Science, University of Toronto, Toronto, CA.

    Google Scholar 

  12. Breiman L., Bagging predictors, Machine Learning 1996; vol. 24, no. 2:123–140.

    MATH  MathSciNet  Google Scholar 

  13. Drucker H., Cortes C., Jackel L. D., Lecun Y., and Vapnik V., Boosting and other ensemble methods, Neural Computation, 1994; vol. 6, no. 6:1289–1301

    Article  MATH  Google Scholar 

  14. Windridge D., Kittler J., “Combined Classifier Optimisation via Feature Selection”, Proceedings “Advances in Pattern Recognition”, Lecture Notes in Computer Science. VOL. 1876

    Google Scholar 

  15. Dietterich T. G., Bakiri G. “Solving Multiclass Learning Problems via Error-Correcting Output Codes.” Journal of Artificial Intelligence Research, 1995; Vol 2.: 263–286.

    MATH  Google Scholar 

  16. R A Jacobs, “Methods for combining experts’ probability assessments”, Neural Computation, 3, pp 79–87, 1991

    Article  Google Scholar 

  17. J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas, “On combining classifiers”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 3, 1998, 226–239

    Article  Google Scholar 

  18. L. Lam and C.Y. Suen, “Optimal combinations of pattern classifiers”, Pattern Recognition Letters, vol. 16, no. 9, 1995, 945–954.

    Article  Google Scholar 

  19. A F R Rahman and M C Fairhurst, “An evaluation of multi-expert configurations for the recognition of handwritten numerals”, Pattern Recognition Letters, 31, pp 1255–1273, 1998

    Google Scholar 

  20. A F R Rahman and M C Fairhurst, “A new hybrid approach in combining multiple experts to recognise handwritten numerals”, Pattern Recognition Letters, 18, pp 781–790, 1997

    Article  Google Scholar 

  21. K Woods, W P Kegelmeyer and K Bowyer, “Combination of multiple classifiers using local accuracy estimates”, IEEE Trans. Pattern Analysis and Machine Intelligence, 19, pp 405–410, 1997

    Article  Google Scholar 

  22. J. Kittler, “Improving Recognition Rates by Classifier Combination: A Review”, 1st IAPR TC1 Workshop on Statistical Techniques in Pattern Recognition, 205–210, June 1997

    Google Scholar 

  23. F. Natterer, Proc. “State of the Art in Numerical Analysis”, York, April 1–4, 1996.

    Google Scholar 

  24. S. Webb, “The Physics of Medical Imaging”, Institute of Physics Publishing, Bristol and Philadelphia, 1993.

    Google Scholar 

  25. G. T. Herman, “Image Reconstruction from Projections, The Fundamentals of Computerized Tomography”, Computer science and Applied Mathematics, 1980.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Electronic and Electrical Engineering, University of Surrey, Guildford, Surrey, GU2 7XH, UK

    David Windridge & Josef Kittler

Authors
  1. David Windridge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Josef Kittler
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, Surrey, GU2 7XH, UK

    Terry Windeatt

  2. Dept. of Electrical and Electronic Engineering, University of Cagliari, Piazza D’Ami, 09123, Cagliari, Italy

    Fabio Roli

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Windridge, D., Kittler, J. (2003). The Practical Performance Characteristics of Tomographically Filtered Multiple Classifier Fusion. In: Windeatt, T., Roli, F. (eds) Multiple Classifier Systems. MCS 2003. Lecture Notes in Computer Science, vol 2709. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44938-8_19

Download citation

  • DOI: https://doi.org/10.1007/3-540-44938-8_19

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40369-2

  • Online ISBN: 978-3-540-44938-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation