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Adaboost Classifier by Artificial Immune System Model

  • Hind Taud
  • Juan Carlos Herrera-Lozada
  • Jesús Álvarez-Cedillo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)

Abstract

An algorithm combining Artificial Immune System and AdaBoost called Imaboost is proposed to improve the feature selection and classification performance. Adaboost is a machine learning technique, which generates a strong classifier as a combination of simple classifiers. In Adaboost, through learning, the search for the best simple classifiers is replaced by the clonal selection algorithm. Haar features extracted from face database are chosen as a case study. A comparison between Adaboost and Imaboost is provided.

Keywords

Artificial immune system Feature selection Adaboost Clonal selection algorithm Haar Features 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hind Taud
    • 1
  • Juan Carlos Herrera-Lozada
    • 2
  • Jesús Álvarez-Cedillo
    • 1
  1. 1.Centro de Innovación y Desarrollo Tecnológico en CómputoInstituto Politécnico NacionalMadero
  2. 2.Centro de Investigación en ComputaciónInstituto Politécnico NacionalMadero

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