Optimal classifier ensemble design for vehicle detection using GAVaPS

Heesung Lee, Jaehun Lee, Euntai Kim

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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
Issue number1
Publication statusPublished - 2010 Jan

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Applied Mathematics


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