Adaboost classifier by artificial immune system model

Authors:
Hind Taud;Juan Carlos Herrera-Lozada;Jesús Álvarez-Cedillo
Affiliations:
Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, México, D.F.;Centro de Investigación en Computación, Instituto Politécnico Nacional, México, D.F.;Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, México, D.F.
Venue:
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Year:
2010

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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.