Abstract
This paper aims to explore the concept of “depth” through the selection of various ensemble methods and proposes a practical deep ensemble learning method. In this study, we propose a nested ensemble learning method. First, we employ the stacking framework for selective ensemble learning. Next, we integrate the stacked ensemble with bagging and boosting techniques to create a comprehensive stacked ensemble. We utilized both domestic and foreign online loan data to build the model and test its ability to generalize. The experimental results demonstrate that the nested ensemble proposed in this paper outperforms models such as logistic regression and support vector machines, showing exceptional generalization ability.
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References
Lixiang, Z.: Reflections on bank credit risk management under the new situation. SME Manag. Technol. 698(05), 127–129 (2023)
Yu, L., Wang, S., Lai, K.K.: Credit risk assessment with a multistage neural network ensemble learning approach. Expert Syst. Appl. 34(2), 1434–1444 (2008)
Zhao ming, W., Xichuan, H.: Algorithm optimization based on LightGBM credit risk control model. Comput. Appl. Software 39(06), 342–349 (2022)
Mengqi, S.: Research on personal loan default prediction based on hybrid feature extraction and ensemble learning. University of Electronic Science and Technology of China (2020). https://doi.org/10.27005/d.cnki.gdzku.004081
Xin yi, W.: Big data risk control model based on GBDT algorithm study. J. zhengzhou aviation ind. Manag. Inst. 42(5), 108–112 (2020). https://doi.org/10.19327/j.carolcarrollnk zuaxb.1007-9734.2020.05.009
Pan, D.: Research on credit risk control strategy of Internet consumer Finance based on Xgboost. Xiangtan University (2018)
Mao guang, W., Xing, Y.: A risk control model and algorithm based on AP-Entropy selection ensemble. Comput. Sci. 48(S2), 71–76+80 (2021)
Khashman, A.: Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes. Expert Syst. Appl. 37(9), 6233–6239 (2010)
Uddin, M.S., Chi, G., Al Janabi, M.A.M., et al.: Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability. Int. J. Finan. Econ. 27(3), 3713–3729 (2022)
Papouskova, M., Hajek, P.: Two-stage consumer credit risk modelling using heterogeneous ensemble learning[J]. Decis. Support Syst. 118, 33–45 (2019)
Kohavi, R., Wolpert, D.H.: Bias plus variance decomposition for zero-one loss functions. In: ICML, vol. 96, pp. 275–83 (1996)
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Wang, M., Cui, Y. (2024). Design and Implementation of Risk Control Model Based on Deep Ensemble Learning Algorithm. In: Shi, Z., Torresen, J., Yang, S. (eds) Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 703. Springer, Cham. https://doi.org/10.1007/978-3-031-57808-3_9
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DOI: https://doi.org/10.1007/978-3-031-57808-3_9
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