Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset
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 strengt...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10399785/ |
| _version_ | 1797348690857295872 |
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| author | Ummey Hany Ainan Lip Yee Por Yen-Lin Chen Jing Yang Chin Soon Ku |
| author_facet | Ummey Hany Ainan Lip Yee Por Yen-Lin Chen Jing Yang Chin Soon Ku |
| author_sort | Ummey Hany Ainan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T12:09:37Z |
| format | Article |
| id | doaj.art-5c27d23ca455416d9f0fae1fb615e58c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T12:09:37Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5c27d23ca455416d9f0fae1fb615e58c2024-01-23T00:04:12ZengIEEEIEEE Access2169-35362024-01-01129369938110.1109/ACCESS.2024.335417310399785Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish DatasetUmmey Hany Ainan0Lip Yee Por1https://orcid.org/0000-0001-5865-1533Yen-Lin Chen2https://orcid.org/0000-0001-7717-9393Jing Yang3Chin Soon Ku4https://orcid.org/0000-0003-0793-3308Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, TaiwanDepartment of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Computer Science, Universiti Tunku Abdul Rahman, Kampar, MalaysiaThe 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.https://ieeexplore.ieee.org/document/10399785/Bankruptcy forecastingpredictive analyticsensemble learninghyperparameter tuningmachine learning |
| spellingShingle | Ummey Hany Ainan Lip Yee Por Yen-Lin Chen Jing Yang Chin Soon Ku Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset IEEE Access Bankruptcy forecasting predictive analytics ensemble learning hyperparameter tuning machine learning |
| title | Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset |
| title_full | Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset |
| title_fullStr | Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset |
| title_full_unstemmed | Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset |
| title_short | Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset |
| title_sort | advancing bankruptcy forecasting with hybrid machine learning techniques insights from an unbalanced polish dataset |
| topic | Bankruptcy forecasting predictive analytics ensemble learning hyperparameter tuning machine learning |
| url | https://ieeexplore.ieee.org/document/10399785/ |
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