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A novel multistage deep belief network based extreme learning machine ensemble learning paradigm for credit risk assessment

Author

Listed:
  • Lean Yu

    (Beijing University of Chemical Technology)

  • Zebin Yang

    (Beijing University of Chemical Technology)

  • Ling Tang

    (Beijing University of Chemical Technology)

Abstract

To achieve high assessment accuracy for credit risk, a novel multistage deep belief network (DBN) based extreme learning machine (ELM) ensemble learning methodology is proposed. In the proposed methodology, three main stages, i.e., training subsets generation, individual classifiers training and final ensemble output, are involved. In the first stage, bagging sampling algorithm is applied to generate different training subsets for guaranteeing enough training data. Second, the ELM, an effective AI forecasting tool with the unique merits of time-saving and high accuracy, is utilized as the individual classifier, and diverse ensemble members can be accordingly formulated with different subsets and different initial conditions. In the final stage, the individual results are fused into final classification output via the DBN model with sufficient hidden layers, which can effectively capture the valuable information hidden in ensemble members. For illustration and verification, the experimental study on one publicly available credit risk dataset is conducted, and the results show the superiority of the proposed multistage DBN-based ELM ensemble learning paradigm in terms of high classification accuracy.

Suggested Citation

  • Lean Yu & Zebin Yang & Ling Tang, 2016. "A novel multistage deep belief network based extreme learning machine ensemble learning paradigm for credit risk assessment," Flexible Services and Manufacturing Journal, Springer, vol. 28(4), pages 576-592, December.
  • Handle: RePEc:spr:flsman:v:28:y:2016:i:4:d:10.1007_s10696-015-9226-2
    DOI: 10.1007/s10696-015-9226-2
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    References listed on IDEAS

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    1. O. L. Mangasarian, 1965. "Linear and Nonlinear Separation of Patterns by Linear Programming," Operations Research, INFORMS, vol. 13(3), pages 444-452, June.
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    3. Ling Tang & Wei Dai & Lean Yu & Shouyang Wang, 2015. "A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(01), pages 141-169.
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    Cited by:

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    2. Yu, Lean & Huang, Xiaowen & Yin, Hang, 2020. "Can machine learning paradigm improve attribute noise problem in credit risk classification?," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 440-455.
    3. Xia, Yufei & Zhao, Junhao & He, Lingyun & Li, Yinguo & Yang, Xiaoli, 2021. "Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1590-1613.
    4. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    5. Chen, Liao & Ma, Shoufeng & Li, Changlin & Yang, Yuance & Wei, Wei & Cui, Runbang, 2024. "A spatial–temporal graph-based AI model for truck loan default prediction using large-scale GPS trajectory data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    6. Yuquan Meng & Yuhang Yang & Haseung Chung & Pil-Ho Lee & Chenhui Shao, 2018. "Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review," Sustainability, MDPI, vol. 10(12), pages 1-28, December.
    7. Xiao, Jin & Zhong, Yu & Jia, Yanlin & Wang, Yadong & Li, Ruoyi & Jiang, Xiaoyi & Wang, Shouyang, 2024. "A novel deep ensemble model for imbalanced credit scoring in internet finance," International Journal of Forecasting, Elsevier, vol. 40(1), pages 348-372.
    8. Yu, Lean & Liang, Shaodong & Chen, Rongda & Lai, Kin Keung, 2022. "Predicting monthly biofuel production using a hybrid ensemble forecasting methodology," International Journal of Forecasting, Elsevier, vol. 38(1), pages 3-20.

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