Combining weighted SMOTE with ensemble learning for the class-imbalanced prediction of small business credit risk

Combining weighted SMOTE with ensemble learning for the class-imbalanced prediction of small business credit risk

Abstract In small business credit risk assessment, the default and nondefault classes are highly imbalanced. To overcome this problem, this study proposes an extended ensemble approach rooted in the weighted synthetic minority oversampling technique (WSMOTE), which is called WSMOTE-ensemble. The pro...

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Bibliographic Details
Main Authors: Mohammad Zoynul Abedin, Chi Guotai, Petr Hajek, Tong Zhang
Format: Article
Language:English
Published: Springer 2022-01-01
Series:Complex & Intelligent Systems
Subjects:
Small business
Credit risk
Imbalanced data
Oversampling
Weighted SMOTE
Ensemble learning
Online Access:https://doi.org/10.1007/s40747-021-00614-4

Internet

https://doi.org/10.1007/s40747-021-00614-4

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