Artificial Intelligence · 10.08.2026, 09:42 UTC
Certified Interpolation Oversampling: Per-Instance Safety Guarantees for Imbalanced Learning
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | arXiv cs.LG ↗ |
| Veröffentlicht | 10.08.2026 UTC |
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arXiv:2501.15790v2 Announce Type: replace Abstract: Synthetic minority oversampling is typically designed and evaluated against a predictive objective, generating samples that improve downstream classification. This paper pursues a second objective by generating samples that carry a stated safety property, established for each instance by construction rather than assumed. We introduce Certified Interpolation Safe Oversampling (CISO), a three-phase interpolation framework built for this objective. A safety-guided distribution selects where synthesis occurs, a locality-and clearance-weighted distribution selects with whom each anchor interpolates, and a q-Gaussian placement density determines how far along the resulting segment each instance is placed. The framework provides three guarantees. Each synthetic instance seeded by a sufficiently safe anchor carries a certified distance from the majority class; a signed temperature parameter provably and monotonically shifts synthesis between boundary-seeking and interior-seeking regimes; and selection weights are strictly positive by construction, so no degenerate case arises. Certification is obtained alongside competitive predictive performance rather than in place of it. Under a protocol preregistered before evaluation, across 45 datasets, four classifiers, and eleven competing methods, CISO is statistically equivalent to SMOTE on precision-recall AUC, ranks second of eleven under gradient boosting, and completes every one of 11,460 fold-level evaluations without failure. A parameter sweep further reveals a continuous …