Enhancing financial risk prediction with symbolic classifiers: addressing class imbalance and the accuracy–interpretability trade–off
Abstract Machine learning for financial risk prediction has garnered substantial interest in recent decades. However, the class imbalance problem and the dilemma of accuracy gain by loss interpretability have yet to be widely studied. Symbolic classifiers have emerged as a promising solution for for...
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| Principais autores: | , , , , , , |
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| Format: | Artigo |
| Sprog: | Inglês |
| Udgivet: |
Springer Nature
2024-11-01
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| Serier: | Humanities & Social Sciences Communications |
| Online adgang: | https://doi.org/10.1057/s41599-024-04047-5 |
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