Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering-Reference-Cited by-同舟云学术

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Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering

Published:2022-01-23 Issue:2 Volume:61 Page:715-741
ISSN:0927-7099
Container-title:Computational Economics
language:en
Short-container-title:Comput Econ

Author:

Ben Jabeur Sami^ORCID,Stef Nicolae^ORCID,Carmona Pedro^ORCID

Funder

Spanish Ministry of Science, Innovation and Universities

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Economics, Econometrics and Finance (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s10614-021-10227-1.pdf

Reference95 articles.

1. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.

2. Alzubi, J. A., Bharathikannan, B., & Tanwar, S. (2019). Boosted neural network ensemble classification for lung cancer disease diagnosis. Applied Soft Computing Journal, 80, 579–591. https://doi.org/10.1016/j.asoc.2019.04.031

3. Ander, J., Arévalo, J., Paredes, R., & Nin, J. (2018). End-to-end neural network architecture for fraud scoring in card payments. Pattern Recognition Letters, 105, 175–181. https://doi.org/10.1016/j.patrec.2017.08.024

4. Bao, W., Lianju, N., & Yue, K. (2019). Integration of unsupervised and supervised machine learning algorithms for credit risk assessment. Expert Systems with Applications, 128, 301–315. https://doi.org/10.1016/j.eswa.2019.02.033

5. Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417. https://doi.org/10.1016/j.eswa.2017.04.006

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