关键词: 信用风险评估, 支持向量机（SVM）, 集成学习, AdaBoost, 随机子集模型

Abstract: Accurate credit risk assessment is very important for risk management of financial institutions. In order to further improve the accuracy of credit risk assessment model, a hybrid ensemble approach, RSA-SVM, is proposed. This approach is a SVM-based ensemble learning method, combined with two popular ensemble strategies, random subspace model and AdaBoost, in order to improve the diversity of base learners, and get a more accurate ensemble learning model. This model is applied on two public credit data sets, and the experimental results show that the RSA-SVM is a more effective model for credit risk assessment.

Key words: credit risk assessment;　Support Vector Machine（SVM）, ensemble learning, AdaBoost, random subspace model