TY - GEN
T1 - Adaboost classifier by artificial immune system model
AU - Taud, Hind
AU - Herrera-Lozada, Juan Carlos
AU - Álvarez-Cedillo, Jesús
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Adaboost
KW - Artificial immune system
KW - Clonal selection algorithm
KW - Feature selection
KW - Haar Features
UR - http://www.scopus.com/inward/record.url?scp=78751502981&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15992-3_19
DO - 10.1007/978-3-642-15992-3_19
M3 - Contribución a la conferencia
SN - 3642159915
SN - 9783642159916
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 171
EP - 179
BT - Advances in Pattern Recognition - Second Mexican Conference on Pattern Recognition, MCPR 2010, Proceedings
T2 - Mexican Conference on Pattern Recognition 2010, MCPR 2010
Y2 - 27 September 2010 through 29 September 2010
ER -