A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique-Reference-Cited by-同舟云学术

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A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique

Published:2021-01 Issue: Volume:98 Page:106852
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Shen Feng,Zhao Xingchao,Kou Gang,Alsaadi Fawaz E.

Funder

National Natural Science Foundation of China

Humanities and Social Sciences Foundation of the Ministry of Education of China

Chinese National Funding of Social Sciences

Applied Basic Research Program of Sichuan Province

Fundamental Research Funds for the Central Universities

Research Center for System Sciences and Enterprise Development

Publisher

Elsevier BV

Subject

Software

Reference40 articles.

1. Statistical and machine learning models in credit scoring: A systematic literature survey;Dastile;Appl. Soft Comput. J.,2020

2. Performance comparison of multiple discriminant analysis and logit models in bankruptcy prediction;Mihalovic;Econ. Sociol.,2016

3. Enterprise credit risk evaluation based on neural network algorithm;Huang;Cogn. Syst. Res.,2018

4. Feature construction for fraudulent credit card cash-out detection;Wu;Decis. Support Syst.,2019

5. Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates;Sun;Inform. Sci.,2018

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