Ainan, Ummey Hany and Por, Lip Yee and Chen, Yen-Lin and Yang, Jing and Ku, Chin Soon (2024) Advancing bankruptcy forecasting with hybrid machine learning techniques: Insights from an unbalanced Polish dataset. IEEE Access, 12. pp. 9369-9381. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2024.3354173.
Full text not available from this repository.Abstract
The challenge of bankruptcy prediction, critical for averting financial sector losses, is amplified by the prevalence of imbalanced datasets, which often skew prediction models. Addressing this, our study introduces the innovative hybrid model XGBoost+ANN, designed to leverage the strengths of both ensemble learning and artificial neural networks. This model integrates a comprehensive set of features with parameters optimized through genetic algorithms, eschewing traditional feature selection approaches. Our research focuses on an unbalanced dataset of Polish companies and reveals that the XGBoost+ANN model, in particular, exhibits outstanding performance. Optimized using genetic algorithms and without feature selection, this model achieved the highest AUC (0.958), sensitivity (0.752), and accuracy (0.983) scores, surpassing other models in our study. This remarkable outperformance, along with the robust results, marks a substantial advancement in the field of bankruptcy prediction. It underscores the efficacy of our approach in addressing the persistent challenge of data imbalance, offering a more reliable and accurate solution for financial risk assessment.
| Item Type: | Article |
|---|---|
| Funders: | National Science and Technology Council in Taiwan |
| Uncontrolled Keywords: | Bankruptcy forecasting; predictive analytics; ensemble learning; hyperparameter tuning; machine learning |
| Subjects: | H Social Sciences > HG Finance Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Computer System & Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 14 Jun 2024 07:52 |
| Last Modified: | 14 Jun 2024 07:52 |
| URI: | http://eprints.um.edu.my/id/eprint/44183 |
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