Vertical bagging decision trees model for credit scoring
Vertical bagging decision trees model for credit scoring
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DOI：
10.1016/j.eswa.2010.04.054
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发表时间：
2010-12
期刊：
影响因子：
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通讯作者：
Defu Zhang;Xiyue Zhou;S. Leung;Jiemin Zheng
中科院分区：
文献类型：
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作者：
Defu Zhang;Xiyue Zhou;S. Leung;Jiemin Zheng
近年来，随着我国消费观念的转变，越来越多的人，尤其是年轻人开始使用信用卡，信用卡业务也随之快速增长。因此，建立信用卡信用评分模型等有效的工具，对信用卡经营决策者提供帮助，具有重要的意义。本文提出了一种新的信用评分模型-垂直装袋决策树模型（VBDTM）。该模型是一种有别于传统装袋方法的新型装袋方法。VBDTM模型通过组合预测属性得到分类器的集合。在VBDTM模型中，所有训练样本和部分属性都参与每个分类器的学习。与传统的Bagging方法不同，传统的Bagging方法是用样本子集训练分类器，每个分类器具有相同的属性。通过UCI机器学习库中的两个信用数据库对VBDTM进行了测试，分析结果表明，该方法在预测精度上表现突出。
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.