Comparison of Classifier Fusion Methods for Classification in Pattern Recognition Tasks

  • Francisco Moreno-Seco
  • José M. Iñesta
  • Pedro J. Ponce de León
  • Luisa Micó
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)


This work presents a comparison of current research in the use of voting ensembles of classifiers in order to improve the accuracy of single classifiers and make the performance more robust against the difficulties that each individual classifier may have. Also, a number of combination rules are proposed. Different voting schemes are discussed and compared in order to study the performance of the ensemble in each task. The ensembles have been trained on real data available for benchmarking and also applied to a case study related to statistical description models of melodies for music genre recognition.


Support Vector Machine Fusion Method Vote Scheme Weighted Vote Classical Music 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Francisco Moreno-Seco
    • 1
  • José M. Iñesta
    • 1
  • Pedro J. Ponce de León
    • 1
  • Luisa Micó
    • 1
  1. 1.Department of Software and Computing SystemsUniversity of AlicanteAlicanteSpain

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