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Cutpoint Selection via Penalization in Credit Scoring

신용평점화에서 벌점화를 이용한 절단값 선택

Jin, Seul-Ki;Kim, Kwang-Rae;Park, Chang-Yi
진슬기;김광래;박창이

  • Received : 2011.10.14
  • Accepted : 2011.12.26
  • Published : 2012.04.30

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.

Keywords

Classification spline machine;coarse classification;credit scorecard

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Cited by

  1. Categorical Variable Selection in Naïve Bayes Classification vol.28, pp.3, 2015, https://doi.org/10.5351/KJAS.2015.28.3.407
  2. Fused least absolute shrinkage and selection operator for credit scoring vol.85, pp.11, 2015, https://doi.org/10.1080/00949655.2014.922685
  3. Developing the high risk group predictive model for student direct loan default using data mining vol.26, pp.6, 2015, https://doi.org/10.7465/jkdi.2015.26.6.1417
  4. Developing the credit risk scoring model for overdue student direct loan vol.27, pp.5, 2016, https://doi.org/10.7465/jkdi.2016.27.5.1293

Acknowledgement

Supported by : 서울시립대학교