The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Cutpoint Selection via Penalization in Credit Scoring

신용평점화에서 벌점화를 이용한 절단값 선택

  • 진슬기 (서울시립대학교 통계학과) ;
  • 김광래 (고려대학교 통계연구소) ;
  • 박창이 (서울시립대학교 통계학과)
  • Received : 2011.10.14
  • Accepted : 2011.12.26
  • Published : 2012.04.30
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Abstract

In constructing a credit scorecard, each characteristic variable is divided into a few attributes; subsequently, weights are assigned to those attributes in a process called coarse classification. While partitioning a characteristic variable into attributes, one should determine appropriate cutpoints for the partition. In this paper, we propose a cutpoint selection method via penalization. In addition, we compare the performances of the proposed method with classification spline machine (Koo et al., 2009) on both simulated and real credit data.

신용평점표(credit scorecard) 작성시 각 특성변수(characteristic variable)들을 몇 개의 속성(attribute)들로 나누고 각 속성에 적절한 가중치를 부여하게 된다. 이 과정을 성김화(coarse classi cation)라 한다. 특성변수들을 속성들로 나눌 때 그 기준이 되는 절단값(cutpoint)을 선택해야 한다. 본 논문에서는 벌점화(penalization) 기반의 절단값 선택법을 제안한다. 또한 여러가지 모의실험과 실제 신용자료의 분석을 통하여 제안된 방법과 기존의 절단값 선택법인 스플라인 분류 기계 (Koo 등, 2009)의 성능을 비교한다.

Keywords

References

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