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A classification spline machine for building a credit scorecard

Authors
Koo, Ja-YongPark, ChangyiJhun, Myoungshic
Issue Date
2009
Publisher
TAYLOR & FRANCIS LTD
Keywords
cutpoint; logistic regression; simulated annealing; spline basis
Citation
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v.79, no.5, pp.681 - 689
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
Volume
79
Number
5
Start Page
681
End Page
689
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/122149
DOI
10.1080/00949650701859577
ISSN
0094-9655
Abstract
In constructing a scorecard, we partition each characteristic variable into a few attributes and assign weights to those attributes. For the task, a simulated annealing algorithm has been proposed. A drawback of simulated annealing is that the number of cutpoints separating each characteristic variable into attributes is required as an input. We introduce a scoring method, called a classification spline machine (CSM), which determines cutpoints automatically via a stepwise basis selection. In this paper, we compare performances of CSM and simulated annealing on simulated datasets. The results indicate that the CSM can be useful in the construction of scorecards.
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정경대학 (통계학과)
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