Vertical bagging decision trees model for credit scoring

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.04.054 * Corresponding author. E-mail addresses: [email protected] (D. Zhan Zhou), [email protected] (S.C.H. Leung). In recent years, more and more people, especially young people, begin to use credit card with the changing of consumption concept in China so that the business on credit cards is growing fast. Therefore, it is significative that some effective tools such as credit-scoring models are created to help those decision makers engaged in credit cards. A novel credit-scoring model, called vertical bagging decision trees model (abbreviated to VBDTM), is proposed for the purpose in this paper. The model is a new bagging method that is different from the traditional bagging. The VBDTM model gets an aggregation of classifiers by means of the combination of predictive attributes. In the VBDTM model, all train samples and just parts of attributes take part in learning of every classifier. By contrast, classifiers are trained with the sample subsets in the traditional bagging method and every classifier has the same attributes. The VBDTM has been tested by two credit databases from the UCI Machine Learning Repository, and the analysis results show that the performance of the method proposed by us is outstanding on the prediction accuracy. 2010 Elsevier Ltd. All rights reserved.