1. 西南财经大学 2. 西南财经大学 中国支付体系研究中心，成都 610074； 3. 西南财经大学 经济信息工程学院，成都 610074 4. Emerging Technology Lab, Samsung Research and Development Institute America-Dallas, Richardson TX 75082, USA； 5. 四川大学 计算机学院，成都 610064
Sparse Bayesian learning for credit risk evaluation
LI Taiyong1,2,WANG Huijun3,WU Jiang1,3,ZHANG Zhilin4,TANG Changjie5
1. Institute of Chinese Payment System, Southwestern University of Finance and Economics, Chengdu Sichuan 610074, China; 2. undefined 3. School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu Sichuan 610074, China; 4. Emerging Technology Lab, Samsung Research and Development Institute America-Dallas, Richardson TX 75082, USA; 5. School of Computer Science, Sichuan University, Chengdu Sichuan 610064, China
Abstract：To solve the low classification accuracy and poor interpretability of selected features in traditional credit risk evaluation, a new model using Sparse Bayesian Learning (SBL) to evaluate personal credit risk (SBLCredit) was proposed in this paper. The SBLCredit utilized the advantages of SBL to get as sparse as possible solutions under the priori knowledge on the weight of features, which led to both good classification performance and effective feature selection. SBLCredit improved the classification accuracy of 4.52%, 6.40%, 6.26% and 2.27% averagely when compared with the state-of-the-art K-Nearest Neighbour (KNN), Nave Bayes, decision tree and support vector machine respectively on real-world German and Australian credit datasets. The experimental results demonstrate that the proposed SBLCredit is a promising method for credit risk evaluation with higher accuracy and fewer features.
2013, Vol. 33 
