1 |
叶强, 刘作仪, 孟庆峰, 等. 互联网金融的国家战略需求和关键科学问题[J]. 中国科学基金, 2016, 30(2): 150-158.
|
|
Ye Q, Liu Z Y, Meng Q F, et al. National strategic demand and key scientific issues in relation to internet finance[J]. Bulletin of National Natural Science Foundation of China, 2016, 30(2): 150-158.
|
2 |
Louzada F, Ara A, Fernandes G B. Classification methods applied to credit scoring: systematic review and overall comparison[J]. Surveys in Operations Research and Management Science, 2016, 21(2): 117-134.
|
3 |
余乐安, 张有德. 基于关联规则赋权特征选择集成的信用分类研究[J]. 系统工程理论与实践, 2020, 40(2): 366-372.
|
|
Yu L A, Zhang Y D. Weight-selected attribute bagging based on association rules for credit dataset classification[J]. System Engineering — Theory & Practice, 2020, 40(2): 366-372.
|
4 |
王钊,蒋翠清,丁勇.基于混合生存分析的动态信用评分方法[J].系统工程理论与实践, 2021, 41(2): 389-399.
|
|
Wang Z, Jiang C Q, Ding Y. Dynamic credit scoring method based on mixture survival analysis[J]. System Engineering—Theory & Practice,2021,41(2): 389-399.
|
5 |
王小燕, 张中艳, 马双鸽. 基于文本先验信息的贷款信用风险评估模型[J]. 中国管理科学, 2021, 29(5): 34-44.
|
|
Wang X Y, Zhang Z Y, Ma S G. A loan credit risk model incorporating text prior information[J]. Chinese Journal of Management Science, 2021, 29(5): 34-44.
|
6 |
王小燕, 袁腾, 段湘斌. 基于零膨胀分位数两部模型的银行贷款违约预测研究[J]. 中国管理科学, 2022, 30(10): 1-13.
|
|
Wang X Y, Yuan T, Duan X B. Loan default forecasting based on zero—inflated quantile two—part model[J]. Chinese Journal of Management Science, 2022, 30(10): 1-13.
|
7 |
Orgler Y E. A credit scoring model for commercial loans[J]. Journal of Money, Credit and Banking, 1970, 2(4): 435-445.
|
8 |
Wiginton J C. A note on the comparison of logit and discriminant models of consumer credit behavior[J]. Journal of Financial and Quantitative Analysis, 1980, 15(3): 757-770.
|
9 |
方匡南, 章贵军, 张惠颖. 基于Lasso-logistic模型的个人信用风险预警方法[J]. 数量经济技术经济研究, 2014, 31(2): 125-136.
|
|
Fang K N, Zhang G J, Zhang H Y. Individual credit risk prediction method: application of a Lasso-logistic model[J]. Journal of Quantitative & Technical Economics, 2014, 31(2): 125-136.
|
10 |
Martens D, Baesens B, Van Gestel T, et al. Comprehensible credit scoring models using rule extraction from support vector machines[J]. European Journal of Operational Research, 2007, 183(3): 1466-1476.
|
11 |
Maldonado S, Pérez J, Bravo C. Cost-based feature selection for support vector machines: an application in credit scoring[J]. European Journal of Operational Research, 2017, 261(2): 656-665.
|
12 |
李建平, 徐伟宣, 刘京礼, 等. 消费者信用评估中支持向量机方法研究[J]. 系统工程, 2004(10): 35-39.
|
|
Li J P, Xu W X, Liu J L, et al. Support vector machines approach to credit evaluation[J]. Systems Engineering, 2004(10): 35-39.
|
13 |
刘京礼, 李建平, 徐伟宣, 等. 信用评估中的鲁棒赋权自适应L_p最小二乘支持向量机方法[J]. 中国管理科学, 2010, 18(5): 28-33.
|
|
Liu J L, Li J P, Xu W X, et al. A robust weighted adaptive LpLS-SVM method for credit risk assessment[J]. Chinese Journal of Management Science, 2010, 18(5): 28-33.
|
14 |
姚潇, 余乐安. 模糊近似支持向量机模型及其在信用风险评估中的应用[J]. 系统工程理论与实践, 2012, 32(3): 549-554.
|
|
Yao X, Yu L A. A fuzzy proximal support vector machine model and its application to credit risk analysis[J]. System Engineering — Theory & Practice, 2012, 32(03): 549-554.
|
15 |
余乐安. 基于最小二乘近似支持向量回归模型的电子商务信用风险预警[J]. 系统工程理论与实践, 2012, 32(3): 508-514.
|
|
Yu L A. E-commerce credit risk early-warning with a least squares proximal support vector regression model[J]. System Engineering — Theory & Practice, 2012, 32(3): 508-514.
|
16 |
陆爱国, 王珏, 刘红卫. 基于改进的SVM学习算法及其在信用评分中的应用[J]. 系统工程理论与实践, 2012, 32(3): 515-521.
|
|
Lu A G, Wang J, Liu H W. An improved SVM learning algorithm and its applications to credit scorings[J]. System Engineering — Theory & Practice, 2012, 32(03): 515-521.
|
17 |
韩璐, 韩立岩. 正交支持向量机及其在信用评分中的应用[J]. 管理工程学报, 2017, 31(2): 128-136.
|
|
Han L, Han L Y. Orthogonal support vector machine and its application in credit scoring[J]. Journal of Industrial Engineering and Engineering Management, 2017, 31(2): 128-136.
|
18 |
黎春, 周振宇. 信用评分模型中拒绝推断问题研究:基于半监督协同训练法的改进[J]. 统计研究, 2019, 36(9): 82-92.
|
|
Li C, Zhou Z Y. Research on reject inference in credit scoring model: based on the improvement of semi-supervised co-training method[J]. Statistical Research, 2019, 36(9): 82-92.
|
19 |
Maldonado S, Paredes G. A semi-supervised approach for reject inference in credit scoring using SVMs[C]//Proceedings of Advances in Data Mining. Applications and Theoretical Aspects: 10th Industrial Conference, Berlin, Germany, July 12-14, 2010. Springer Berlin Heidelberg, 2010: 558-571.
|
20 |
Huang S C, Tang Y C, Lee C W, et al. Kernel local Fisher discriminant analysis based manifold-regularized SVM model for financial distress predictions[J]. Expert Systems with Applications, 2012, 39(3): 3855-3861.
|
21 |
Li Z, Tian Y, Li K, et al. Reject inference in credit scoring using semi-supervised support vector machines[J]. Expert Systems with Applications: An International Journal, 2017, 74(C): 105-114.
|
22 |
Shen F, Yang Z, Zhao X, et al. Reject inference in credit scoring using a three-way decision and safe semi-supervised support vector machine[J]. Information Sciences, 2022, 606: 614-627.
|
23 |
Yang Y, Zou H. A fast unified algorithm for solving group-lasso penalize learning problems[J]. Statistics and Computing, 2015, 25(6): 1129-1141.
|
24 |
Collobert R, Sinz F, Weston J, et al. Large scale transductive SVMs[J]. Journal of Machine Learning Research, 2006(7): 1687-1712.
|