Posted by on 2017年7月3日

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1.什么是个人征信

Credit information is a crucial element in the evaluation of credit risk. Convolutional Neural Network and Retrieval Convolutional Neural Network;
Potentially useful indicators of default risk include a borrower’s credit scores (Iyer et al. 2009), demographic information (Kumar 2007), and social net- work (Lin et al. 2013; Liu et al. 2015).

2.网络文章:

大数据与神经网络算法,能否用来分析银行信贷企业的数据,得出关键指标和权重?
AlphaGo 的数据算法,能否用来分析银行信贷企业的各项数据,然后得出关键指标和权重?
HOW TO USE MACHINE LEARNING IN CREDIT SCORING

Nowadays, the hottest machine learning algorithms could be categorized as being either single classifiers or ensemble classifiers. The representatives of the single classifiers are CART, Naive Bayes, SVM, logistics regression. The modification of single classifiers by logit of bagging and boosting (and their derivatives such as Adaboost) are widely used, such as Random Forests, CART-Adaboost, etc.

3.Reference:

Huang, Z., Chen, H., Hsu, C. J., Chen, W. H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision support systems, 37(4), 543-558.
Chicago
Wang, Y., Wang, S., & Lai, K. K. (2005). A new fuzzy support vector machine to evaluate credit risk. IEEE Transactions on Fuzzy Systems, 13(6), 820-831.
Hayashi, Y., Tanaka, Y., Takagi, T., Saito, T., Iiduka, H., Kikuchi, H., ... & Mitra, S. (2016). Recursive-rule extraction algorithm with J48graft and applications to generating credit scores. Journal of Artificial Intelligence and Soft Computing Research, 6(1), 35-44.
Huang, Z., Chen, H., Hsu, C. J., Chen, W. H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision support systems, 37(4), 543-558.
Chicago
Huang, C. L., Chen, M. C., & Wang, C. J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert systems with applications, 33(4), 847-856.
Chicago
Cho, Y. H., Kim, J. K., & Kim, S. H. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert systems with Applications, 23(3), 329-342.
Chicago

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  1. 一些学习资料的整理 – 饱蠹阁baoduge - […] PERSONALIZED CREDIT RATING […]

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