Using Set Learning to Improve Set-Valued Classifiers on Long-tailed Data
Set-valued classification (SVC) offers a practical approach to communicating uncertainty while maintaining accuracy by predicting variable-sized sets per input rather than fixed-sized sets (e.g. top-1 or top-5). However, set-valued classifiers (SVCs) exhibit significantly degraded performance on long-tailed and imbalanced datasets, a setting for which they are ostensibly well-suited. This becomes apparent under macro-average evaluation, which weighs all classes equally. SVCs are typically formed during a post-hoc calibration stage, so little is known about how training dynamics affect their behaviour. In this work, we investigate how different neural network training strategies and loss f...
| Authors | Kevin Kasa, Scott C. Lowe, Graham W. Taylor |
|---|---|
| Primary category | cs.LG |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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