Skip to main content
SpringerLink
Log in

Combining Multiple Classifiers with Dynamic Weighted Voting

  • Conference paper
Hybrid Artificial Intelligence Systems (HAIS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5572))

Included in the following conference series:

Abstract

When a multiple classifier system is employed, one of the most popular methods to accomplish the classifier fusion is the simple majority voting. However, when the performance of the ensemble members is not uniform, the efficiency of this type of voting generally results affected negatively. In this paper, new functions for dynamic weighting in classifier fusion are introduced. Experimental results demonstrate the advantages of these novel strategies over the simple voting scheme.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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.

Similar content being viewed by others

References

  1. Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning 36, 105–139 (1999)

    Article  Google Scholar 

  2. Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)

    MATH  Google Scholar 

  3. Breiman, L.: Arcing classifiers. Annals of Statistics 26, 801–823 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  4. Dasarathy, B.V.: Nearest Neighbor Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamos (1991)

    Google Scholar 

  5. Demšar, J.: Statistical comparison of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  6. Dudani, S.A.: The distance weighted k-nearest neighbor rule. IEEE Trans. on System, Man, and Cybernetics 6, 325–327 (1976)

    Article  Google Scholar 

  7. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proc. 13th Intl. Conf. on Machine Learning, pp. 148–156 (1996)

    Google Scholar 

  8. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting, Technical report, Stanford University (1998)

    Google Scholar 

  9. Hansen, L.K., Salomon, P.: Neural network ensembles. IEEE Trans. on Pattern Analysis and Machine Intelligence 12, 993–1001 (1990)

    Article  Google Scholar 

  10. Kuncheva, L.I., Bezdek, J.C., Duin, R.P.W.: Decision templates for multiple classifier fusion. Pattern Recognition 34, 299–314 (2001)

    Article  MATH  Google Scholar 

  11. Matan, O.: On voting ensembles of classifiers. In: Proc. 13th Natl. Conf. on Artificial Intelligence, pp. 84–88 (1996)

    Google Scholar 

  12. Raudys, S.: Trainable fusion rules. I. Large sample size case. Neural Networks 19, 1506–1516 (2006)

    Article  MATH  Google Scholar 

  13. Raudys, S.: Trainable fusion rules. II. Small sample-size effects. Neural Networks 19, 1517–1527 (2006)

    Article  MATH  Google Scholar 

  14. Shepard, R.N.: Toward a universal law of generalization for psychological science. Science 237, 1317–1323 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  15. Woods, K., Kegelmeyer Jr., W.P., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Trans. on Pattern Analysis and Machine Intelligence 19, 405–410 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Grupo de Cómputo Aplicado, Centro Universitario UAEM Valle de Chalco, Av. Hermenegildo Galeana no. 3, 56615, Valle de Chalco, México

    R. M. Valdovinos

  2. Dept. Llenguatges i Sistemes Informàtics, Universitat Jaume I, Av. Sos Baynat s/n, E-12071, Castelló de la Plana, Spain

    J. S. Sánchez

Authors
  1. R. M. Valdovinos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. S. Sánchez
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Grupo de Investigación GICAP, Área de Lenguajes Higher Polytechnic School, Universidad de Burgos, Burgos, Spain

    Emilio Corchado

  2. Department of Computer Science, University of Vermont, 33 Colchester Avenue, Burlington, VT, USA

    Xindong Wu

  3. Computer and Information Science, Helsinki University of Technology, P.O. Box 5400, 02015 HUT, Finland

    Erkki Oja

  4. Grupo de Investigación GICAP, Área de Lenguajes y Sistemas Informáticos, Departamento de Ingeniería Civil, Escuela Politécnica Superior, Universidad de Burgos, Campus Vena, Francisco de Vitoria, 09006, Burgos, Spain

    Álvaro Herrero  & Bruno Baruque  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Valdovinos, R.M., Sánchez, J.S. (2009). Combining Multiple Classifiers with Dynamic Weighted Voting. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_61

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02319-4_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02318-7

  • Online ISBN: 978-3-642-02319-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation