Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Smoothing Kaplan-Meier estimate using monotone support vector regression

단조 서포트벡터기계를 이용한 카플란-마이어 생존함수의 평활

  • 황창하 (단국대학교 정보통계학과) ;
  • 심주용 (인제대학교 데이터정보학과)
  • Received : 2012.08.30
  • Accepted : 2012.09.30
  • Published : 2012.11.30
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Abstract

Support vector machine is known to be the very useful statistical method in classification and nonlinear function estimation. In this paper we propose a monotone support vector regression (SVR) for the estimation of monotonically decreasing function. The proposed monotone SVR is applied to smooth the Kaplan-Meier estimate of survival function. Experimental results are then presented which indicate the performance of the proposed monotone SVR using survival functions obtained by exponential distribution.

서포트벡터 기계는 분류 및 비선형 함수추정에서 유용하게 사용되고 있는 통계적 기법이다. 본 논문에서는 두 개의 입력변수와 회귀함수의 단조 관계를 이용하여 단조 서포트벡터기계를 제안하고, Kaplan-Meier의 방법에 의해서 생존함수의 추정값이 주어진 경우 제안된 방법을 이용하여 생존 함수를 평활하는 방법 또한 제안한다. 모의실험에서는 실제 생존함수를 이용하여 Kaplan-Meier의 방법에 의한 생존함수의 추정값과의 성능을 비교함으로써 제안된 방법의 우수성을 보이기로 한다.

Keywords

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