Machine learning and decision support system on credit scoring-Reference-Cited by-同舟云学术

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Machine learning and decision support system on credit scoring

Published:2019-10-24 Issue:14 Volume:32 Page:9809-9826
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Teles Gernmanno^ORCID,Rodrigues Joel J. P. C.,Saleem Kashif,Kozlov Sergei,Rabêlo Ricardo A. L.

Funder

National Funding from the FCT - Fundação para a Ciência e a Tecnologia

Government of the Russian Federation

Instituto Nacional de Telecomunicações (Inatel), Brazil

Narodowe Centrum Badań i Rozwoju

Deanship of Scientific Research at King Saud University

Brazilian National Council for Research and Development

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-019-04537-7.pdf

Reference56 articles.

1. Akkoç S (2012) An empirical comparison of conventional techniques, neural networks and the three stage hybrid adaptive neuro fuzzy inference system (ANFIS) model for credit scoring analysis: the case of Turkish credit card data. Eur J Oper Res 222:168–178. https://doi.org/10.1016/j.ejor.2012.04.009

2. Lahsasna A, Ainon RN, Wah TY (2010) Credit scoring models using soft computing methods: a survey. Int Arab J Inf Technol 7:115–123

3. Chen W, Xiang G, Liu Y, Wang K (2012) Credit risk evaluation by hybrid data mining technique. Syst Eng Procedia 3:194–200. https://doi.org/10.1016/j.sepro.2011.10.029

4. Ibn UF, Panford JK, Ben H-J (2014) Fuzzy logic approach to credit scoring for micro finances in Ghana (A case study of KWIQPLUS money lending). Int J Comput Appl 94(8):8887. https://doi.org/10.5120/16362-5772

5. Zamula A, Kavun S (2017) Complex systems modeling with intelligent control elements. Int J Model Simul Sci Comput 08:1750009. https://doi.org/10.1142/S179396231750009X

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