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Cutpoint Selection via Penalization in Credit Scoring
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 Title & Authors
Cutpoint Selection via Penalization in Credit Scoring
Jin, Seul-Ki; Kim, Kwang-Rae; Park, Chang-Yi;
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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.
 Keywords
Classification spline machine;coarse classification;credit scorecard;
 Language
Korean
 Cited by
1.
Developing the high risk group predictive model for student direct loan default using data mining, Journal of the Korean Data and Information Science Society, 2015, 26, 6, 1417  crossref(new windwow)
2.
Categorical Variable Selection in Naïve Bayes Classification, Korean Journal of Applied Statistics, 2015, 28, 3, 407  crossref(new windwow)
3.
Fused least absolute shrinkage and selection operator for credit scoring, Journal of Statistical Computation and Simulation, 2015, 85, 11, 2135  crossref(new windwow)
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