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Cross-validation and ensemble analyses on multiple-criteria linear programming classification for credit cardholder behavior. (English) Zbl 1102.91339

Bubak, Marian (ed.) et al., Computational science – ICCS 2004. 4th international conference, Kraków, Poland, June 6–9, 2004. Proceedings, Part IV. Berlin: Springer (ISBN 3-540-22129-8/pbk). Lecture Notes in Computer Science 3039, 931-939 (2004).
Summary: In credit card portfolio management, predicting the cardholders’ behavior is a key to reduce the charge off risk of credit card issuers. As a promising data mining approach, multiple criteria linear programming (MCLP) has been successfully applied to classify credit cardholders’ behavior into two or multiple-groups for business intelligence. The objective of this paper is to study the stability of MCLP in classifying credit cardholders’ behavior by using cross-validation and ensemble techniques. An overview of the two-group MCLP model formulation and a description of the dataset used in this paper are introduced first. Then cross-validation and ensemble methods are tested respectively. As the results demonstrated, the classification rates of cross-validation and ensemble methods are close to the rates of using MCLP alone. In other words, MCLP is a relatively stable method in classifying credit cardholders’ behavior.
For the entire collection see [Zbl 1051.68007].

MSC:

91B30 Risk theory, insurance (MSC2010)
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