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A novel ensemble classification model based on neural networks and a classifier optimisation technique for imbalanced credit risk evaluation

Author

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  • Shen, Feng
  • Zhao, Xingchao
  • Li, Zhiyong
  • Li, Ke
  • Meng, Zhiyi

Abstract

Significant research has been performed on credit risk evaluation, with many machine learning and data mining techniques being employed for financial decision-making. The back propagation (BP) neural network has been a popular choice for credit risk evaluation problems, but many studies have found classifier ensembles to be superior to single classifiers. In this paper, a novel ensemble model based on the synthetic minority over-sampling technique (SMOTE) and a classifier optimisation technique is proposed for personal credit risk evaluation. To mitigate the negative effects of imbalanced datasets on the performance of the credit evaluation model, the SMOTE technique is used to rebalance the target training dataset. The particle swarm optimisation (PSO) algorithm is employed to search for the best-connected weights and deviations in the BP neural networks. Based on the optimised BP neural network classifiers, an ensemble model is developed that combines the AdaBoost approach with the base classifiers. To ensure that the proposed model provides accurate and stable performance, we thoroughly explore and discuss the optimal parameters for the ensemble classification model. Finally, the proposed ensemble model is tested on German and Australian real-world imbalanced datasets. The results demonstrate that this model is more effective at processing credit data problems compared to the other classification models examined in this study.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:phsmap:v:526:y:2019:i:c:s0378437119306582
    DOI: 10.1016/j.physa.2019.121073
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    Citations

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    Cited by:

    1. 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.
    2. Andrés Alonso & José Manuel Carbó, 2022. "Accuracy of explanations of machine learning models for credit decisions," Working Papers 2222, Banco de España.
    3. Oguz Koc & Omur Ugur & A. Sevtap Kestel, 2023. "The Impact of Feature Selection and Transformation on Machine Learning Methods in Determining the Credit Scoring," Papers 2303.05427, arXiv.org.
    4. Guansan Du & Frank Elston, 2022. "RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models," Operations Management Research, Springer, vol. 15(3), pages 925-940, December.
    5. Fisnik Doko & Slobodan Kalajdziski & Igor Mishkovski, 2021. "Credit Risk Model Based on Central Bank Credit Registry Data," JRFM, MDPI, vol. 14(3), pages 1-17, March.
    6. Cui, Xin & Wang, Panpan & Sensoy, Ahmet & Nguyen, Duc Khuong & Pan, Yuying, 2022. "Green Credit Policy and Corporate Productivity: Evidence from a Quasi-natural Experiment in China," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    7. Samami, Maryam & Akbari, Ebrahim & Abdar, Moloud & Plawiak, Pawel & Nematzadeh, Hossein & Basiri, Mohammad Ehsan & Makarenkov, Vladimir, 2020. "A mixed solution-based high agreement filtering method for class noise detection in binary classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    8. Ming-Fu Hsu & Ying-Shao Hsin & Fu-Jiing Shiue, 2022. "Business analytics for corporate risk management and performance improvement," Annals of Operations Research, Springer, vol. 315(2), pages 629-669, August.
    9. Shen, Feng & Zhang, Xin & Wang, Run & Lan, Dao & Zhou, Wei, 2022. "Sequential optimization three-way decision model with information gain for credit default risk evaluation," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1116-1128.

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