Yang, M., Lim, M. K. , Qu, Y., Li, X. and Ni, D. (2022) Repair missing data to improve corporate credit risk prediction accuracy with multi-layer perceptron. Soft Computing, 26(18), pp. 9167-9178. (doi: 10.1007/s00500-022-07277-4)
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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.
Item Type: | Articles |
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Additional Information: | 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]. |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Lim, Professor Ming |
Authors: | Yang, M., Lim, M. K., Qu, Y., Li, X., and Ni, D. |
College/School: | College of Social Sciences > Adam Smith Business School > Management |
Journal Name: | Soft Computing |
Publisher: | Springer |
ISSN: | 1432-7643 |
ISSN (Online): | 1433-7479 |
Published Online: | 07 July 2022 |
Copyright Holders: | Copyright © 2022 The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature |
First Published: | First published in Soft Computing 26(18): 9167-9178 |
Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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