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Averaged Conservative Boosting: Introducing a New Method to Build Ensembles of Neural Networks

  • Joaquín Torres-Sospedra
  • Carlos Hernández-Espinosa
  • Mercedes Fernández-Redondo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4668)

Abstract

In this paper, a new algorithm called Averaged Conservative Boosting (ACB) is presented to build ensembles of neural networks. In ACB we mix the improvements that Averaged Boosting (Aveboost) and Conservative Boosting (Conserboost) made to Adaptive Boosting (Adaboost). In the algorithm we propose we have applied the conservative equation used in Conserboost along with the averaged procedure used in Aveboost in order to update the sampling distribution used in the training of Adaboost. We have tested the methods with seven databases from the UCI repository. The results show that the best results are provided by our method, Averaged Conservative Boosting.

Keywords

Sampling Distribution Single Network Multilayer Feedforward Network Wisconsin Breast Cancer Previous 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 2007

Authors and Affiliations

  • Joaquín Torres-Sospedra
    • 1
  • Carlos Hernández-Espinosa
    • 1
  • Mercedes Fernández-Redondo
    • 1
  1. 1.Departamento de Ingenieria y Ciencia de los Computadores, Universitat Jaume I, Avda. Sos Baynat s/n, C.P. 12071, CastellonSpain

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