Optimal classifier ensemble design for vehicle detection using GAVaPS

Heesung Lee, Jaehun Lee, Euntai Kim

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)96-100
Number of pages5
JournalJournal of Institute of Control, Robotics and Systems
Volume16
Issue number1
DOIs
Publication statusPublished - 2010 Jan 1

Fingerprint

Classifier Ensemble
Vehicle Detection
Population Size
Classifiers
Genetic algorithms
Genetic Algorithm
Classifier
Search Space
Demonstrate
Experiment
Design
Experiments

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 journalArticle

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