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Can machine learning paradigm improve attribute noise problem in credit risk classification?

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  • Yu, Lean
  • Huang, Xiaowen
  • Yin, Hang

Abstract

In this paper, a dual-voting-based learning paradigm is proposed to solve attribute noise problem in credit risk classification. In the proposed learning paradigm, three stages are involved. In the first stage, four indexes are introduced to evaluate the noise level of attributes. In the second stage, attributes with different noise levels are divided into different attribute sets in accordance with the dual-voting results of noise level. In the third stage, credit datasets with different attributes sets are dealt with different learning strategies and different de-noising methods for comparison purpose. In the proposed learning paradigm, a classification and regression tree (CART) model is adopted as the generic classifier to evaluate the performance on training datasets generated by different learning strategies and noise reduction methods. In addition, the performance of all learning strategies on sparse data with attribute noise is also discussed. Experimental results show that the proposed learning paradigm performs better than the benchmarks to solve the attribute noise problem not only in accuracy and its stability, but also in speediness. Further analysis indicates that the sparse data with attribute noise can further improve the stability of accuracy for a specific de-noising method. This implies that the proposed dual voting-based learning paradigm is a promising solution to attribute noise reduction in credit risk classification.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reveco:v:70:y:2020:i:c:p:440-455
    DOI: 10.1016/j.iref.2020.08.016
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    1. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    2. Wilson X. B. Li & Tina T. He & Stella P. L. Cho, 2019. "Government involvement in banking systems and economic growth: a comparison across countries," Economic and Political Studies, Taylor & Francis Journals, vol. 7(1), pages 35-65, January.
    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. R Florez-Lopez, 2010. "Effects of missing data in credit risk scoring. A comparative analysis of methods to achieve robustness in the absence of sufficient data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 486-501, March.
    6. 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.
    7. Feder, Gershon & Just, Richard E., 1977. "A study of debt servicing capacity applying logit analysis," Journal of Development Economics, Elsevier, vol. 4(1), pages 25-38, February.
    8. Gang Kou & Yi Peng & Yong Shi & Morgan Wise & Weixuan Xu, 2005. "Discovering Credit Cardholders’ Behavior by Multiple Criteria Linear Programming," Annals of Operations Research, Springer, vol. 135(1), pages 261-274, March.
    9. Qinhua Xu & William Chung, 2018. "Risk assessment of China’s Belt and Road Initiative’s sustainable investing: a data envelopment analysis approach," Economic and Political Studies, Taylor & Francis Journals, vol. 6(3), pages 319-337, July.
    10. Werner, Richard A., 2016. "A lost century in economics: Three theories of banking and the conclusive evidence," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 361-379.
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    Cited by:

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    3. González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2023. "Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. Zhou, Yang & Xie, Chi & Wang, Gang-Jin & Zhu, You & Uddin, Gazi Salah, 2023. "Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).

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