A novel multistage deep belief network based extreme learning machine ensemble learning paradigm for credit risk assessment-Reference-Cited by-同舟云学术

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A novel multistage deep belief network based extreme learning machine ensemble learning paradigm for credit risk assessment

Published:2015-09-29 Issue:4 Volume:28 Page:576-592
ISSN:1936-6582
Container-title:Flexible Services and Manufacturing Journal
language:en
Short-container-title:Flex Serv Manuf J

Author:

Yu Lean,Yang Zebin,Tang Ling

Funder

National Science Fund for Distinguished Young Scholars

National Natural Science Foundation of China (CN)

National Program for Support of Top-Notch Young Professionals

Fundamental Research Funds for the Central Universities in BUCT

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research

Link

http://link.springer.com/content/pdf/10.1007/s10696-015-9226-2.pdf

Reference37 articles.

1. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

2. Chi BW, Hsu CC (2012) A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model. Expert Syst Appl 39(3):2650–2661

3. Grablowsky BJ, Talley WK (1981) Probit and discriminant functions for classifying credit applicants—a comparison. J Econ Bus 33(3):254–261

4. Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal 12(10):993–1001

5. Henley WE, Hand DJ (1996) A k-nearest-neighbour classifier for assessing consumer credit risk. Statistician 45:77–95

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