The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Comparison Studies of Classification Methods based on L1-Distance and L1-Data Depth

L1-거리와 L1-데이터뎁스를 이용한 분류방법의 비교연구

  • Baek Soo-Jin (Division of Bacterial Respiratory Infections, Center for Infectious Disease Research, Korea Center for Disease Control & Prevention, Korean National Institute of Health) ;
  • Hwang Jin-Soo (Department of Statistics, Inha University) ;
  • Kim Jean-Kyung (Department of Statistics, Inha University)
  • 백수진 (국립보건연구원 질병관리본부 호흡기세균팀) ;
  • 황진수 (인하대학교 자연과학대학 수학통계학부) ;
  • 김진경 (인하대학교 자연과학대학 수학통계학부)
  • Published : 2006.03.01
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Abstract

We consider a new classification method(DnDclass) combining two classification rules based on $L_1$-distance(L1DISTclass) and $L_1$-data depth(L1DDclass). To investigate characteristics and to evaluate the performance of these classification methods, we use simulation data in various settings. Through this simulation study, we can confirm that the new method, DnDclass, performs relatively well in many cases.

$L_1$-데이터뎁스를 이용한 분류방법(L1DDclass)과 관측치들 사이의 $L_1$-거리를 이용한 분류방법(L1DISTclass)의 특징을 살펴보고, 이 두 방법을 결합한 새로운 분류방법 (DnDclass: Distance and Data-depth based classification)의 효용성을 소개하고자 한다. 모의실험을 통해 세가지 분류방법의 결과를 비교하고 제안된 분류방법이 다양한 경우에 더 효과적일 수 있다는 사실을 확인한다.

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References

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