Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 96-100 |
| Number of pages | 5 |
| Journal | Journal of Institute of Control, Robotics and Systems |
| Volume | 16 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2010 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Applied Mathematics
Cite this
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Optimal classifier ensemble design for vehicle detection using GAVaPS. / Lee, Heesung; Lee, Jaehun; Kim, Euntai.
In: Journal of Institute of Control, Robotics and Systems, Vol. 16, No. 1, 01.01.2010, p. 96-100.Research output: Contribution to journal › Article
TY - JOUR
T1 - Optimal classifier ensemble design for vehicle detection using GAVaPS
AU - Lee, Heesung
AU - Lee, Jaehun
AU - Kim, Euntai
PY - 2010/1/1
Y1 - 2010/1/1
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
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 -