Defining attributes for scorecard construction in credit scoring
AbstractIn many domains, simple forms of classification rules are needed because of requirements such as ease of use. A particularly simple form splits each variable into just a few categories, assigns weights to the categories, sums the weights for a new object to be classified, and produces a classification by comparing the score with a threshold. Such instruments are often called scorecards. We describe a way to find the best partition of each variable using a simulated annealing strategy. We present theoretical and empirical comparisons of two such additive models, one based on weights of evidence and another based on logistic regression.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Journal of Applied Statistics.
Volume (Year): 27 (2000)
Issue (Month): 5 ()
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- Izabela Majer, 2006. "Application scoring: logit model approach and the divergence method compared," Working Papers, Department of Applied Econometrics, Warsaw School of Economics 17, Department of Applied Econometrics, Warsaw School of Economics.
- Robert Till & David Hand, 2003. "Behavioural models of credit card usage," Journal of Applied Statistics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 30(10), pages 1201-1220.
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