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Repair missing data to improve corporate credit risk prediction accuracy with multi-layer perceptron

  • Data analytics and machine learning
  • Published:
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Abstract

Data loss has become an inevitable phenomenon in corporate credit risk (CCR) prediction. To ensure the integrity of data information for subsequent analysis and prediction, it is essential to repair the missing data as accurately as possible. To solve the problem of missing data in credit classification, this study proposes a multi-layer perceptron ensemble (MLP–ESM) model that can perform data interpolation and prediction simultaneously to predict CCR. The model makes full use of non-missing information and interpolates more missing columns with fewer missing values. In this way, not only the data features needed for missing data interpolation are extracted, but also the structural relationship features between the predicted target and the existing data are extracted, which can achieve the effect of simultaneous interpolation and prediction. The results show that the MLP–ESM model can effectively interpolate and predict the missing dataset of CCR. The prediction accuracy is 83.11%, which is better than the traditional machine learning model. This fully shows that the dataset after interpolation can achieve a better prediction effect.

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Data availability

The datasets generated during and/or analyzed during the current study are not publicly available due to data being private but are available from the corresponding author on reasonable request.

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Funding

This work was supported by the graduate research and innovation foundation of Chongqing, China [Grant No. CYS21047] and 2022 Scientific Research Startup Fund of Chongqing Jiaotong University [Grant No. F1210045].

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Authors and Affiliations

  1. School of Economics and Business Administration, Chongqing University, Chongqing, 400030, People’s Republic of China

    Mei Yang & Yingchi Qu

  2. School of Management, Nanjing University of Posts and Telecommunications, Jiangsu, 210003, People’s Republic of China

    Du Ni

  3. School of Economics and Management, Chongqing Jiaotong University, Chongqing, 400074, People’s Republic of China

    Xingzhi Li

  4. Adam Smith Business School, University of Glasgow, Glasgow, G14 8QQ, UK

    Ming K. Lim

Authors
  1. Mei Yang
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  2. Ming K. Lim
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  3. Yingchi Qu
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  4. Xingzhi Li
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  5. Du Ni
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Contributions

MY: Conceptualization, Investigation, Writing—original draft, Validation, Visualization. MKL: Conceptualization, Project administration, Validation. YQ: Validation, Visualization. XL: Conceptualization, Investigation, Validation. DN: Conceptualization, Methodology, Project administration.

Corresponding author

Correspondence to Du Ni.

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The authors declare no conflict of interest.

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This work does not contain any studies with human participants or animals performed by any of the authors.

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Informed consent was obtained from all individual participants included in the study.

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Yang, M., Lim, M.K., Qu, Y. et al. Repair missing data to improve corporate credit risk prediction accuracy with multi-layer perceptron. Soft Comput 26, 9167–9178 (2022). https://doi.org/10.1007/s00500-022-07277-4

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