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New Results on Combination Methods for Boosting Ensembles

  • Joaquín Torres-Sospedra
  • Carlos Hernández-Espinosa
  • Mercedes Fernández-Redondo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5163)

Abstract

The design of an ensemble of neural networks is a procedure that can be decomposed into two steps. The first one consists in generating the ensemble, i.e., training the networks with significant differences. The second one consists in combining properly the information provided by the networks. Adaptive Boosting, one of the best performing ensemble methods, has been studied and improved by some authors including us. Moreover, Adaboost and its variants use a specific combiner based on the error of the networks. Unfortunately, any deep study on combining this kind of ensembles has not been done yet. In this paper, we study the performance of some important ensemble combiners on ensembles previously trained with Adaboost and Aveboost. The results show that an extra increase of performance can be provided by applying the appropriate combiner.

Keywords

Combination Method Single Network Correct Class Borda Count Ensemble Neural Network 
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 2008

Authors and Affiliations

  • Joaquín Torres-Sospedra
    • 1
  • Carlos Hernández-Espinosa
    • 1
  • Mercedes Fernández-Redondo
    • 1
  1. 1.Departamento de Ingenieria y Ciencia de los ComputadoresUniversitat Jaume ICastellonSpain

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