Next Article in Journal
Least Squares Approximation of Flatness on Riemannian Manifolds
Next Article in Special Issue
Analysis on the Efficiency of Risk Management in the Chinese Listed Companies
Previous Article in Journal
Uniformly Resolvable Decompositions of Kv-I into n-Cycles and n-Stars, for Even n
Previous Article in Special Issue
Storytelling Advertising Investment Profits in Marketing: From the Perspective of Consumers’ Purchase Intention
Article

A Comparative Performance Assessment of Ensemble Learning for Credit Scoring

College of Management and Economics, Tianjin University, Tianjin 300072, China
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(10), 1756; https://doi.org/10.3390/math8101756
Received: 5 September 2020 / Revised: 28 September 2020 / Accepted: 29 September 2020 / Published: 13 October 2020
(This article belongs to the Special Issue Mathematical Analysis in Economics and Management)
Extensive research has been performed by organizations and academics on models for credit scoring, an important financial management activity. With novel machine learning models continue to be proposed, ensemble learning has been introduced into the application of credit scoring, several researches have addressed the supremacy of ensemble learning. In this research, we provide a comparative performance evaluation of ensemble algorithms, i.e., random forest, AdaBoost, XGBoost, LightGBM and Stacking, in terms of accuracy (ACC), area under the curve (AUC), Kolmogorov–Smirnov statistic (KS), Brier score (BS), and model operating time in terms of credit scoring. Moreover, five popular baseline classifiers, i.e., neural network (NN), decision tree (DT), logistic regression (LR), Naïve Bayes (NB), and support vector machine (SVM) are considered to be benchmarks. Experimental findings reveal that the performance of ensemble learning is better than individual learners, except for AdaBoost. In addition, random forest has the best performance in terms of five metrics, XGBoost and LightGBM are close challengers. Among five baseline classifiers, logistic regression outperforms the other classifiers over the most of evaluation metrics. Finally, this study also analyzes reasons for the poor performance of some algorithms and give some suggestions on the choice of credit scoring models for financial institutions. View Full-Text
Keywords: credit scoring; ensemble learning; baseline classifiers; comparative assessment credit scoring; ensemble learning; baseline classifiers; comparative assessment
Show Figures

Figure 1

MDPI and ACS Style

Li, Y.; Chen, W. A Comparative Performance Assessment of Ensemble Learning for Credit Scoring. Mathematics 2020, 8, 1756. https://doi.org/10.3390/math8101756

AMA Style

Li Y, Chen W. A Comparative Performance Assessment of Ensemble Learning for Credit Scoring. Mathematics. 2020; 8(10):1756. https://doi.org/10.3390/math8101756

Chicago/Turabian Style

Li, Yiheng, and Weidong Chen. 2020. "A Comparative Performance Assessment of Ensemble Learning for Credit Scoring" Mathematics 8, no. 10: 1756. https://doi.org/10.3390/math8101756

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop