A Framework for the Analysis of Majority Voting

  • Anand M. Narasimhamurthy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)

Abstract

Majority voting is a very popular combination scheme both because of its simplicity and its performance on real data. A number of earlier studies have attempted a theoretical analysis of majority voting. Many of them assume independence of the classifiers while deriving analytical expressions. We propose a framework which does not incorporate any assumptions. For a binary classification problem, given the accuracies of the classifiers in the team, the theoretical upper and lower bounds for performance obtained by combining them through majority voting are shown to be solutions of a linear programming problem. The framework is general and could provide insight into majority voting.

Keywords

IEEE Transaction Linear Programming Problem Majority Vote Machine Intelligence Venn Diagram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    S. Berg. Condorcet’s jury theorem, dependency among jurors. Social Choice Welfare, 10:87–95, 1993.MATHCrossRefGoogle Scholar
  2. 2.
    P. J. Boland. Majority systems and the condorcet jury theorem. Statistician, 38:181–189, 1989.CrossRefGoogle Scholar
  3. 3.
    Tin Kam Ho, Jonathan J. Hull, and Sargur N. Srihari. Decision combination in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(1):66–75, January 1994.CrossRefGoogle Scholar
  4. 4.
    Josef Kittler, Mohamad Hatef, Robert P. W. Duin, and Jiri Matas. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3):226–239, March 1998.CrossRefGoogle Scholar
  5. 5.
    L. I. Kuncheva, C. J. Whitaker, C.A. Shipp, and R.P.W. Duin. Limits on the majority vote accuracy in classifier fusion. Pattern Analysis and Applications.Google Scholar
  6. 6.
    Ludmila I. Kuncheva. A theoretical study on six classifier fusion strategies. IEEE Transactions. on Pattern Analysis and Machine Intelligence, 24(2):281–286, February 2002.CrossRefGoogle Scholar
  7. 7.
    Ludmila I. Kuncheva and C. J. Whitaker. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning, 51:181–207, 2003.MATHCrossRefGoogle Scholar
  8. 8.
    L. Lam and C.Y. Suen. Application of majority voting to pattern recognition: An analysis of its behavior and performance. IEEE Transactions on Systems, Man and Cybernetics, 27(5):553–568, 1997.CrossRefGoogle Scholar
  9. 9.
    David J. Miller and Lian Yan. Critic-driven ensemble classification. IEEE Transactions on. Signal Processing, 47(10):2833–2844, October 1999.CrossRefGoogle Scholar
  10. 10.
    L. Xu, A. Krzyzak, and C.Y. Suen. Methods of combining multiple classifiers and their application to handwriting recognition. IEEE Transactions on Systems, Man and Cybernetics, 22:418–435, 1992.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Anand M. Narasimhamurthy
    • 1
  1. 1.Department of Computer Science and EngineeringThe Pennsylvania State UniversityUniversity Park

Personalised recommendations