TY - JOUR
T1 - Optimal classifier ensemble design for vehicle detection using GAVaPS
AU - Lee, Heesung
AU - Lee, Jaehun
AU - Kim, Euntai
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2010/1
Y1 - 2010/1
N2 - This paper proposes novel genetic design of optimal classifier ensemble for vehicle detection using Genetic Algorithm with Varying Population Size (GAVaPS). Recently, many classifiers are used in classifier ensemble to deal with tremendous amounts of data. However the problem has a exponential large search space due to the increasing the number of classifier pool. To solve this problem, we employ the GAVaPS which outperforms comparison with simple genetic algorithm (SGA). Experiments are performed to demonstrate the efficiency of the proposed method.
AB - This paper proposes novel genetic design of optimal classifier ensemble for vehicle detection using Genetic Algorithm with Varying Population Size (GAVaPS). Recently, many classifiers are used in classifier ensemble to deal with tremendous amounts of data. However the problem has a exponential large search space due to the increasing the number of classifier pool. To solve this problem, we employ the GAVaPS which outperforms comparison with simple genetic algorithm (SGA). Experiments are performed to demonstrate the efficiency of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=84860711013&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84860711013&partnerID=8YFLogxK
U2 - 10.5302/J.ICROS.2010.16.1.096
DO - 10.5302/J.ICROS.2010.16.1.096
M3 - Article
AN - SCOPUS:84860711013
VL - 16
SP - 96
EP - 100
JO - Journal of Institute of Control, Robotics and Systems
JF - Journal of Institute of Control, Robotics and Systems
SN - 1976-5622
IS - 1
ER -