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Adaptively Promoting Diversity in a Novel Ensemble Method for Imbalanced Credit-Risk Evaluation

Author

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  • Yitong Guo

    (School of Business Administration, Northeastern University, Shenyang 110819, China)

  • Jie Mei

    (School of Business Administration, Northeastern University, Shenyang 110819, China)

  • Zhiting Pan

    (School of Business Administration, Northeastern University, Shenyang 110819, China)

  • Haonan Liu

    (School of Business Administration, Northeastern University, Shenyang 110819, China)

  • Weiwei Li

    (School of Business Administration, Northeastern University, Shenyang 110819, China)

Abstract

Ensemble learning techniques are widely applied to classification tasks such as credit-risk evaluation. As for most credit-risk evaluation scenarios in the real world, only imbalanced data are available for model construction, and the performance of ensemble models still needs to be improved. An ideal ensemble algorithm is supposed to improve diversity in an effective manner. Therefore, we provide an insight in considering an ensemble diversity-promotion method for imbalanced learning tasks. A novel ensemble structure is proposed, which combines self-adaptive optimization techniques and a diversity-promotion method (SA-DP Forest). Additional artificially constructed samples, generated by a fuzzy sampling method at each iteration, directly create diverse hypotheses and address the imbalanced classification problem while training the proposed model. Meanwhile, the self-adaptive optimization mechanism within the ensemble simultaneously balances the individual accuracy as the diversity increases. The results using the decision tree as a base classifier indicate that SA-DP Forest outperforms the comparative algorithms, as reflected by most evaluation metrics on three credit data sets and seven other imbalanced data sets. Our method is also more suitable for experimental data that are properly constructed with a series of artificial imbalance ratios on the original credit data set.

Suggested Citation

  • Yitong Guo & Jie Mei & Zhiting Pan & Haonan Liu & Weiwei Li, 2022. "Adaptively Promoting Diversity in a Novel Ensemble Method for Imbalanced Credit-Risk Evaluation," Mathematics, MDPI, vol. 10(11), pages 1-21, May.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1790-:d:822544
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    References listed on IDEAS

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    1. Shorouq Fathi Eletter & Saad Ghaleb Yaseen & Ghaleb Awad Elrefae, 2010. "Neuro-Based Artificial Intelligence Model for Loan Decisions," American Journal of Economics and Business Administration, Science Publications, vol. 2(1), pages 27-34, March.
    2. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    3. Shen, Feng & Zhao, Xingchao & Li, Zhiyong & Li, Ke & Meng, Zhiyi, 2019. "A novel ensemble classification model based on neural networks and a classifier optimisation technique for imbalanced credit risk evaluation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    4. Koutanaei, Fatemeh Nemati & Sajedi, Hedieh & Khanbabaei, Mohammad, 2015. "A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring," Journal of Retailing and Consumer Services, Elsevier, vol. 27(C), pages 11-23.
    5. Jinyan Li & Lian-sheng Liu & Simon Fong & Raymond K Wong & Sabah Mohammed & Jinan Fiaidhi & Yunsick Sung & Kelvin K L Wong, 2017. "Adaptive Swarm Balancing Algorithms for rare-event prediction in imbalanced healthcare data," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-25, July.
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    Cited by:

    1. Wanying Song & Jian Min & Jianbo Yang, 2023. "Credit Risk Assessment of Heavy-Polluting Enterprises: A Wide- ℓ p Penalty and Deep Learning Approach," Mathematics, MDPI, vol. 11(16), pages 1-19, August.

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