Knowledge distillation with resampling for imbalanced data classification: Enhancing predictive performance and explainability stability
Machine learning classification models often struggle with imbalanced datasets, leading to poor performance in minority classes. While preprocessing approaches like resampling can improve minority class detection, they may introduce sampling bias and reduce model explainability. This study proposes...
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302401658X |
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