TY - BOOK
T1 - Feature selection combining genetic algorithm and adaboost classifiers
AU - Chouaib, H.
AU - Ramos Terrades, Oriol
AU - Tabbone, S.
AU - Cloppet, F.
AU - Vincent, N.
PY - 2008
Y1 - 2008
N2 - This paper presents a fast method using simple genetic algorithms (GAs) for features selection. Unlike traditional approaches using GAs, we have used the combination of Adaboost classifiers to evaluate an individual of the population. So, the fitness function we have used is defined by the error rate of this combination. This approach has been implemented and tested on the MNIST database and the results confirm the effectiveness and the robustness of the proposed approach.
AB - This paper presents a fast method using simple genetic algorithms (GAs) for features selection. Unlike traditional approaches using GAs, we have used the combination of Adaboost classifiers to evaluate an individual of the population. So, the fitness function we have used is defined by the error rate of this combination. This approach has been implemented and tested on the MNIST database and the results confirm the effectiveness and the robustness of the proposed approach.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-77958073138&partnerID=MN8TOARS
U2 - 10.1109/ICPR.2008.4761264
DO - 10.1109/ICPR.2008.4761264
M3 - Proceeding
SN - 978-1-4244-2174-9
BT - Feature selection combining genetic algorithm and adaboost classifiers
ER -