Optimal classifier ensemble design for vehicle detection using GAVaPS

Heesung Lee; Jaehun Lee; Euntai Kim

doi:10.5302/J.ICROS.2010.16.1.096

Optimal classifier ensemble design for vehicle detection using GAVaPS

Heesung Lee, Jaehun Lee, Euntai Kim

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

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	https://doi.org/10.5302/J.ICROS.2010.16.1.096
Publication status	Published - 2010 Jan

All Science Journal Classification (ASJC) codes

Software
Control and Systems Engineering
Applied Mathematics

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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.",

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