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A heterogeneous ensemble credit scoring model based on adaptive classifier selection: An application on imbalanced data

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  • Tong Zhang
  • Guotai Chi

Abstract

In the domain of credit scoring, the number of bad clients is far less than that of good ones. So imbalanced data classification is a realisitc and critical issue in the credit scoring process. In this study, a novel heterogeneous ensemble credit scoring model is proposed for the problem of imbalanced data classification. This proposed model is on basis of five standard classifiers, namely LSVM, KNN, MDA, DT, LR, and adaptively selects the base classifiers with highest AUC according to the data distribution, then integrates all base classifiers to obtain a prediction. Finally, by using five comprehensive performance measures and four classical credit datasets, we find that the proposed model is better than other baseline models. This novel model can be applied to actual credit scoring and assist financial institutions in credit risk management.

Suggested Citation

  • Tong Zhang & Guotai Chi, 2021. "A heterogeneous ensemble credit scoring model based on adaptive classifier selection: An application on imbalanced data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4372-4385, July.
    • Handle: RePEc:wly:ijfiec:v:26:y:2021:i:3:p:4372-4385
    DOI: 10.1002/ijfe.2019
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    References listed on IDEAS

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    1. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(3), pages 757-770, September.
    2. Miguel Almunia & Agustín Bénétrix & Barry Eichengreen & Kevin H. O’Rourke & Gisela Rua, 2010. "From Great Depression to Great Credit Crisis: similarities, differences and lessons [Germany: Guns, butter, and economic miracles]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 25(62), pages 219-265.
    3. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    4. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    5. Yang, Yingxu, 2007. "Adaptive credit scoring with kernel learning methods," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1521-1536, December.
    6. Longstaff, Francis A., 2010. "The subprime credit crisis and contagion in financial markets," Journal of Financial Economics, Elsevier, vol. 97(3), pages 436-450, September.
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    Cited by:

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    2. Linhui Wang & Jianping Zhu & Chenlu Zheng & Zhiyuan Zhang, 2024. "Incorporating Digital Footprints into Credit-Scoring Models through Model Averaging," Mathematics, MDPI, vol. 12(18), pages 1-15, September.
    3. Song, Yingying & Chen, Xinxin, 2025. "Which opinion is more trustworthy: An analysts’ earnings forecast quality assessment framework based on machine learning," The North American Journal of Economics and Finance, Elsevier, vol. 75(PB).

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