Close to Reality: Interpretable and Feasible Data Augmentation for Imbalanced Learning
Many machine learning classification tasks involve imbalanced datasets, which are often subject to over-sampling techniques aimed at improving model performance. However, these techniques are prone to generating unrealistic or infeasible samples. Furthermore, they often function as black boxes, lacking interpretability in their procedures. This opacity makes it difficult to track their effectiveness and provide necessary adjustments, and they may ultimately fail to yield significant performance improvements. To bridge this gap, we introduce the Decision Predicate Graphs for Data Augmentation (DPG-da), a framework that extracts interpretable decision predicates from trained models to captu...
| Authors | Matheus Camilo da Silva, Gabriel Gustavo Costanzo, Andrea de Lorenzo et al. |
|---|---|
| Primary category | cs.LG |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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