A Weighted Majority Vote Strategy Using Bayesian Networks

  • Luigi P. Cordella
  • Claudio De Stefano
  • Francesco Fontanella
  • Alessandra Scotto di Freca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)

Abstract

Most of the methods for combining classifiers rely on the assumption that the experts to be combined make uncorrelated errors. Unfortunately, this theoretical assumption is not easy to satisfy in practical cases, thus effecting the performance obtainable by applying any combination strategy. We tried to solve this problem by explicitly modeling the dependencies among the experts through the estimation of the joint probability distributions among the outputs of the classifiers and the true class. In this paper we propose a new weighted majority vote rule, that uses the joint probabilities of each class as weights for combining classifier outputs. A Bayesian Network automatically infers the joint probability distribution for each class. The final decision is made by taking into account both the votes received by each class and the statistical behavior of the classifiers. The experimental results confirmed the effectiveness of the proposed method.

Keywords

Bayesian Network Joint Probability Direct Acyclic Graph Majority Vote Joint Probability Distribution 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Luigi P. Cordella
    • 1
  • Claudio De Stefano
    • 2
  • Francesco Fontanella
    • 2
  • Alessandra Scotto di Freca
    • 2
  1. 1.Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione (DIETI)Università di Napoli Federico IIItaly
  2. 2.Dipartimento di Ingegneria Elettrica e dell’Informazione (DIEI)Università di Cassino e del Lazio MeridionaleItaly

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