A Hybrid Approach of Boosting Against Noisy Data

  • Emna Bahri
  • Stephane Lallich
  • Nicolas Nicoloyannis
  • Maddouri Mondher
Part of the Studies in Computational Intelligence book series (SCI, volume 165)


To reduce error in generalization, a great number of work is carried out on the classifiers aggregation methods in order to improve generally, by voting techniques, the performance of a single classifier. Among these methods of aggregation, we find the Boosting which is most practical thanks to the adaptive update of the distribution of the examples aiming at increasing in an exponential way the weight of the badly classified examples. However, this method is blamed because of overfitting, and the convergence speed especially with noise. In this study, we propose a new approach and modifications carried out on the algorithm of AdaBoost. We will demonstrate that it is possible to improve the performance of the Boosting, by exploiting assumptions generated with the former iterations to correct the weights of the examples. An experimental study shows the interest of this new approach, called hybrid approach.


Error Rate Hybrid Approach Noisy Data Current Iteration Generalization Error 
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 2009

Authors and Affiliations

  • Emna Bahri
    • 1
    • 2
  • Stephane Lallich
    • 1
    • 2
  • Nicolas Nicoloyannis
    • 1
    • 2
  • Maddouri Mondher
    • 1
    • 2
  1. 1.ERIC Laboratory- 5University of Lyon 2Bron cedexFrance
  2. 2.INSAT zone urbaine la charguia IITunisTunisie

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