A classification spline machine for building a credit scorecard

Ja Yong Koo, Changyi Park, Myoungshic Jhun

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish
Pages (from-to)681-689
Number of pages9
JournalJournal of Statistical Computation and Simulation
Issue number5
StatePublished - 2009


  • Cutpoint
  • Logistic regression
  • Simulated annealing
  • Spline basis


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