Explainable Domain Adaptation Learning Framework for Credit Scoring in Internet Finance Through Adversarial Transfer Learning and Ensemble Fusion Model
Adversarial transfer learning is extensively applied in computer vision owing to its remarkable capability in addressing domain adaptation. However, its applications in credit scoring remain underexplored due to the complexity of financial data. The performance of traditional credit scoring models r...
Saved in:
| Main Authors: | Feiyang Xu, Runchi Zhang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/7/1045 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
NOTE: non-parametric oversampling technique for explainable credit scoring
by: Seongil Han, et al.
Published: (2024-10-01) -
Adversarial Multitask Learning for Domain Adaptation Through Domain Adapter
by: Hidayaturrahman, et al.
Published: (2024-01-01) -
Toward Enhanced Adversarial Robustness Generalization in Object Detection: Feature Disentangled Domain Adaptation for Adversarial Training
by: Yoojin Jung, et al.
Published: (2024-01-01) -
Explainability-driven adversarial robustness assessment for generalized deepfake detectors
by: Lorenzo Cirillo, et al.
Published: (2025-08-01) -
Cross-domain lung opacity detection via adversarial learning and box fusion
by: Jun Yao, et al.
Published: (2024-12-01)