Classifier Ensemble Generation for the Majority Vote Rule

  • Carlos Orrite
  • Mario Rodríguez
  • Francisco Martínez
  • Michael Fairhurst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)


This paper addresses the problem of classifier ensemble generation. The goal is to obtain an ensemble to achieve maximum recognition gains with the lowest number of classifiers. The final decision is taken following a majority vote rule. If the classifiers make independent errors, the majority vote outperforms the best classifier. Therefore, the ensemble should be formed by classifiers exhibiting individual accuracy and diversity. To account for the quality of the ensemble, this work uses a sigmoid function to measure the behavior of the ensemble in relation to the majority vote rule, over a test labelled data set.


Combining classifiers Ensemble generation Majority vote 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Carlos Orrite
    • 1
  • Mario Rodríguez
    • 1
  • Francisco Martínez
    • 1
  • Michael Fairhurst
    • 2
  1. 1.Aragon Institute for Engineering ResearchUniversity of ZaragozaSpain
  2. 2.Electronics DepartmentUniversity of KentUK

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