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

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
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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