Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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kNNDD-based One-Class Classification by Nonparametric Density Estimation

비모수 추정방법을 활용한 kNNDD의 이상치 탐지 기법

  • Son, Jung-Hwan (School of Industrial Management Engineering, Korea University) ;
  • Kim, Seoung-Bum (School of Industrial Management Engineering, Korea University)
  • 손정환 (고려대학교 산업경영공학부) ;
  • 김성범 (고려대학교 산업경영공학부)
  • Received : 2012.02.20
  • Accepted : 2012.06.12
  • Published : 2012.09.01
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Abstract

One-class classification (OCC) is one of the recent growing areas in data mining and pattern recognition. In the present study we examine a k-nearest neighbors data description (kNNDD) algorithm, one of the OCC algorithms widely used. In particular, we propose to use nonparametric estimation methods to determine the threshold of the kNNDD algorithm. A simulation study has been conducted to explore the characteristics of the proposed approach and compare it with the existing approach that determines the threshold. The results demonstrate the usefulness and flexibility of the proposed approach.

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References

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