Skip to main content
Springer Nature Link
Log in

Ensemble Confidence Estimates Posterior Probability

  • Conference paper
Multiple Classifier Systems (MCS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3541))

Included in the following conference series:

  • 1989 Accesses

Abstract

We have previously introduced the Learn + +  algorithm that provides surprisingly promising performance for incremental learning as well as data fusion applications. In this contribution we show that the algorithm can also be used to estimate the posterior probability, or the confidence of its decision on each test instance. On three increasingly difficult tests that are specifically designed to compare posterior probability estimates of the algorithm to that of the optimal Bayes classifier, we have observed that estimated posterior probability approaches to that of the Bayes classifier as the number of classifiers in the ensemble increase. This satisfying and intuitively expected outcome shows that ensemble systems can also be used to estimate confidence of their output.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Kuncheva, L.I.: Combining Pattern Classifiers, Methods and Algorithms. Wiley Interscience, Hoboken (2004)

    Book  MATH  Google Scholar 

  2. Freund, Y., Schapire, R.: A decision theoretic generalization of on-line learning and an application to boosting. Computer and System Sci. 57(1), 119–139 (1997)

    Article  MathSciNet  Google Scholar 

  3. Kuncheva, L.I.: A Theoretical Study on Six Classifier Fusion Strategies. IEEE Trans. Pattern Analysis and Machine Intelligence 24(2), 281–286 (2002)

    Article  Google Scholar 

  4. Lewitt, M., Polikar, R.: An ensemble approach for data fusion with Learn++. In: Windeatt, T., Roli, F. (eds.) MCS 2003. LNCS, vol. 2709, pp. 176–186. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  5. Polikar, R., Udpa, L., Udpa, S., Honavar, V.: Learn + + : An incremental learning algorithm for supervised neural networks. IEEE Trans. on System, Man and Cybernetics (C) 31(4), 497–508 (2001)

    Article  Google Scholar 

  6. Duin, R.P., Tax, M.: Classifier conditional posterior probabilities. In: Amin, A., Pudil, P., Dori, D. (eds.) SPR 1998 and SSPR 1998. LNCS, vol. 1451, pp. 611–619. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  7. Duda, R., Hart, P., Stork, D.: In: Pattern Classification 2/e, Ch. 3&4, pp. 80–214. Wiley Interscience, New York (2001)

    Google Scholar 

  8. Alpaydin, E., Jordan, M.: Local linear perceptrons for classification. IEEE Transactions on Neural Networks 7(3), 788–792 (1996)

    Article  Google Scholar 

  9. Wilson, D., Martinez, T.: Combining cross-validation and confidence to measure fitness. In: IEEE Joint Conf. on Neural Networks, vol. 2, pp. 1409–1414 (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical and Computer Engineering, Rowan University, 201 Mullica Hill Rd., Glassboro, NJ, 08028, USA

    Michael Muhlbaier, Apostolos Topalis & Robi Polikar

Authors
  1. Michael Muhlbaier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Apostolos Topalis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robi Polikar
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. NASA Ames Research Center, Mail Stop 269-1, 94035-1000, Moffett Field, CA, USA

    Nikunj C. Oza

  2. Signal Processing and Pattern Recognition Laboratory, Electrical and Computer Engineering, Rowan University, 08028, Glassboro, NJ, USA

    Robi Polikar

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

    Josef Kittler

  4. University of Cagliari, Department of Electrical and Electronic Engineering, Piazza d’Armi, 09123, Cagliari, Italy

    Fabio Roli

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Muhlbaier, M., Topalis, A., Polikar, R. (2005). Ensemble Confidence Estimates Posterior Probability. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_33

Download citation

  • DOI: https://doi.org/10.1007/11494683_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26306-7

  • Online ISBN: 978-3-540-31578-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics