摘要
针对传统信用评估方法分类精度低、特征可解释性差等问题,提出了一种使用稀疏贝叶斯学习方法来进行个人信用评估的模型(SBLCredit)。SBLCredit充分利用稀疏贝叶斯学习的优势,在添加的特征权重的先验知识的情况下进行求解,使得特征权重尽量稀疏,以此实现个人信用评估和特征选择。在德国和澳大利亚真实信用数据集上,SBLCredit方法的分类精度比传统的K近邻、朴素贝叶斯、决策树和支持向量机平均提高了4.52%,6.40%,6.26%和2.27%。实验结果表明,SBLCredit分类精度高,选择的特征少,是一种有效的个人信用评估方法。
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）, Na＇fvc 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.
出处
《计算机应用》
CSCD
北大核心
2013年第11期3094-3096,3148,共4页
journal of Computer Applications
基金
教育部人文社会科学研究青年基金资助项目(11YJCZH084)
中央高校基本科研业务专项资金资助项目(JBK130142,JBK130503)
西南财经大学科研基金资助项目(2011XG130)
关键词
稀疏贝叶斯学习
分类
信用评估
金融风险
特征选择
Sparse Bayesian Learning （SBL）
classification
credit risk evaluation
financial risk
feature selection
