Skip to main content

Combining the Results in Pairwise Classification Using Dempster-Shafer Theory: A Comparison of Two Approaches

  • Conference paper
Artificial Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6113))

Included in the following conference series:

  • 1785 Accesses

Abstract

The paper deals with the multi-class (polychotomous) classification problem solved using an ensemble of binary classifiers. The use of the Dempster-Shafer theory to combine the results of binary classifiers is studied. Two approaches are proposed and compared. Some experimental results are provided.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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.

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. Gromisz, M.: On the application of an ensemble of classifiers for data preselection. In: Hołubiec, J. (ed.) Systems Analysis for Financial and Management Applications, vol. 11, EXIT, Warszawa (2009) (in Polish)

    Google Scholar 

  3. Fürnkranz, J.: Round robin classification. J. of Mach. Learn. Res. 2, 721–747 (2002)

    Article  MATH  Google Scholar 

  4. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. J. Wiley & Sons, Chichester (2001)

    MATH  Google Scholar 

  5. Shafer, G.: A mathematical theory of evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  6. Smets, P., Kennes, R.: The transferable belief model. Artif. Intell. 66(2), 191–234 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  7. Yager, R.: On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans. on Syst., Man and Cybern. 18, 183–190 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  8. Quost, B., Denoeux, T., Masson, M.H.: Pairwise classifier combination using belief functions. Pattern Recognition Letters 28(5), 644–653 (2007)

    Article  Google Scholar 

  9. Burger, T., Aran, O., Caplier, A.: Modeling hesitation and conflict: A belief-based approach for multi-class problems. In: Proceedings of the 5th ICMLA Conference, Washington, DC, USA, pp. 95–100. IEEE Computer Society, Los Alamitos (2006)

    Google Scholar 

  10. Denoeux, T.: Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recognition 30(7), 1095–1107 (1997)

    Article  Google Scholar 

  11. Yager, R.R.: Decision making under Dempster-Shafer uncertainties. In: Yager, R.R., Liu, L. (eds.) Classic Works of the Dempster-Shafer Theory of Belief Functions. Studies in Fuzziness and Soft Computing, vol. 219, pp. 619–632. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  12. Smets, P.: The degree of belief in a fuzzy event. Inf. Sci. 25(1), 1–19 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  13. Asuncion, A., Newman, D.: UCI machine learning repository (2007)

    Google Scholar 

  14. Clark, D., Schreter, Z., Adams, A.: A quantitative comparison of dystal and backpropagation. In: Proceedings of the Seventh ACNN Conference, Australia (1996)

    Google Scholar 

  15. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Gromisz, M., Zadrożny, S. (2010). Combining the Results in Pairwise Classification Using Dempster-Shafer Theory: A Comparison of Two Approaches. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13208-7_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13207-0

  • Online ISBN: 978-3-642-13208-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics