Cross-validation and ensemble analyses on multiple-criteria linear programming classification for credit cardholder behavior

Abstract

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. © Springer-Verlag Berlin Heidelberg 2004.