Robust Asymmetric Adaboost

  • Pablo Ormeño
  • Felipe Ramírez
  • Carlos Valle
  • Héctor Allende-Cid
  • Héctor Allende
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)


In real world pattern recognition problems, such as computer-assisted medical diagnosis, events of a given phenomena are usually found in minority, making it necessary to build algorithms that emphasize the effect of one of the classes at training time. In this paper we propose a variation of the well-known Adaboost algorithm that is able to improve its performance by using an asymmetric and robust cost function. We assess the performance of the proposed method on two medical datasets and synthetic datasets with different levels of imbalance and compare our results against three state-of-the-art ensemble learning approaches, achieving better and comparable results.


ensemble learning adaboost asymmetric cost functions robust methods 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pablo Ormeño
    • 1
  • Felipe Ramírez
    • 1
  • Carlos Valle
    • 1
  • Héctor Allende-Cid
    • 1
  • Héctor Allende
    • 1
    • 2
  1. 1.Departamento de InformáticaUniversidad Técnica Federico Santa MaríaValparaísoChile
  2. 2.Factultad de Ingeniería y CienciaUniversidad Adolfo IbáñezViña del MarChile

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