Credit Rating Model Based on Improved TabNet
Under the rapid evolution of financial technology, traditional credit risk management paradigms relying on expert experience and singular algorithmic architectures have proven inadequate in addressing complex decision-making demands arising from dynamically correlated multidimensional risk factors a...
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MDPI AG
2025-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/9/1473 |
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| author | Shijie Wang Xueyong Zhang |
| author_facet | Shijie Wang Xueyong Zhang |
| author_sort | Shijie Wang |
| collection | DOAJ |
| description | Under the rapid evolution of financial technology, traditional credit risk management paradigms relying on expert experience and singular algorithmic architectures have proven inadequate in addressing complex decision-making demands arising from dynamically correlated multidimensional risk factors and heterogeneous data fusion. This manuscript proposes an enhanced credit rating model based on an improved TabNet framework. First, the Kaggle “Give Me Some Credit” dataset undergoes preprocessing, including data balancing and partitioning into training, testing, and validation sets. Subsequently, the model architecture is refined through the integration of a multi-head attention mechanism to extract both global and local feature representations. Bayesian optimization is then employed to accelerate hyperparameter selection and automate a parameter search for TabNet. To further enhance classification and predictive performance, a stacked ensemble learning approach is implemented: the improved TabNet serves as the feature extractor, while XGBoost (Extreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), CatBoost (Categorical Boosting), KNN (K-Nearest Neighbors), and SVM (Support Vector Machine) are selected as base learners in the first layer, with XGBoost acting as the meta-learner in the second layer. The experimental results demonstrate that the proposed TabNet-based credit rating model outperforms benchmark models across multiple metrics, including accuracy, precision, recall, F1-score, AUC (Area Under the Curve), and KS (Kolmogorov–Smirnov statistic). |
| format | Article |
| id | doaj-art-ec1e31f70ef74c548a7a5613da1b2e7d |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-ec1e31f70ef74c548a7a5613da1b2e7d2025-08-20T02:30:46ZengMDPI AGMathematics2227-73902025-04-01139147310.3390/math13091473Credit Rating Model Based on Improved TabNetShijie Wang0Xueyong Zhang1School of Finance, Central University of Finance and Economics, Beijing 102206, ChinaSchool of Finance, Central University of Finance and Economics, Beijing 102206, ChinaUnder the rapid evolution of financial technology, traditional credit risk management paradigms relying on expert experience and singular algorithmic architectures have proven inadequate in addressing complex decision-making demands arising from dynamically correlated multidimensional risk factors and heterogeneous data fusion. This manuscript proposes an enhanced credit rating model based on an improved TabNet framework. First, the Kaggle “Give Me Some Credit” dataset undergoes preprocessing, including data balancing and partitioning into training, testing, and validation sets. Subsequently, the model architecture is refined through the integration of a multi-head attention mechanism to extract both global and local feature representations. Bayesian optimization is then employed to accelerate hyperparameter selection and automate a parameter search for TabNet. To further enhance classification and predictive performance, a stacked ensemble learning approach is implemented: the improved TabNet serves as the feature extractor, while XGBoost (Extreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), CatBoost (Categorical Boosting), KNN (K-Nearest Neighbors), and SVM (Support Vector Machine) are selected as base learners in the first layer, with XGBoost acting as the meta-learner in the second layer. The experimental results demonstrate that the proposed TabNet-based credit rating model outperforms benchmark models across multiple metrics, including accuracy, precision, recall, F1-score, AUC (Area Under the Curve), and KS (Kolmogorov–Smirnov statistic).https://www.mdpi.com/2227-7390/13/9/1473TabNetstackingcredit ratingcredit risk |
| spellingShingle | Shijie Wang Xueyong Zhang Credit Rating Model Based on Improved TabNet Mathematics TabNet stacking credit rating credit risk |
| title | Credit Rating Model Based on Improved TabNet |
| title_full | Credit Rating Model Based on Improved TabNet |
| title_fullStr | Credit Rating Model Based on Improved TabNet |
| title_full_unstemmed | Credit Rating Model Based on Improved TabNet |
| title_short | Credit Rating Model Based on Improved TabNet |
| title_sort | credit rating model based on improved tabnet |
| topic | TabNet stacking credit rating credit risk |
| url | https://www.mdpi.com/2227-7390/13/9/1473 |
| work_keys_str_mv | AT shijiewang creditratingmodelbasedonimprovedtabnet AT xueyongzhang creditratingmodelbasedonimprovedtabnet |