Using Machine Learning to Predict the Defaults of Credit Card Clients

Author:

Le Tuan,Pham Tan,Dao Son

Publisher

Springer Singapore

Reference23 articles.

1. [PDF] The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients|Semantic Scholar. https://www.semanticscholar.org/paper/The-comparisons-of-data-mining-techniques-for-the-Yeh-Lien/1cacac4f0ea9fdff3cd88c151c94115a9fddcf33. Accessed 6 Sep 2020

2. Quantitative methods in credit management: a survey. Oper Res. https://pubsonline.informs.org/doi/abs/10.1287/opre.42.4.589. Accessed 6 Sep 2020

3. Bayesian data mining, with application to benchmarking and credit scoring—Giudici—2001. Applied stochastic models in business and industry. Wiley Online Library. https://onlinelibrary.wiley.com/doi/abs/10.1002/asmb.425. Accessed 6 Sep 2020

4. Lee T-S, Chiu C-C, Lu C-J, Chen I-F (2002) Credit scoring using the hybrid neural discriminant technique. Expert Syst Appl 23(3):245–254. https://doi.org/10.1016/S0957-4174(02)00044-1

5. Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res 5:1205–1224

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