Credit scoring using ensemble machine learning

摘要：

In this study, we applied ensemble machine learning to evaluate credit scoring. With decision tree as the baseline algorithm, two popular ensemble learning methods, bagging and boosting, were evaluated across different experiment conditions: using all 14 features, using selected 6 features on Australian credit data form UCI data set. Results showed that in experiments with all features improved performance was achieved by ensemble learning. The best result was obtained in adaboost CART with 14 features, in which the overall correct rate increases from 83.25％ to 85.86％.

关键词： credit scoring ensemble machine learning bagging adaboost CART

作者: Ping Yao

作者单位: School of Economics & Management, Heilongjiang Institute of Science and Technology, Harbin, 150027, China

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-3

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）