A Model for Imbalanced Label Aggregation: A Focus on Minority-Class Detection
We study imbalanced crowdsourcing with a focus on class-dependent annotator accuracy, a setting that, to the best of our knowledge, remains relatively underexplored despite its importance in real-world inspection systems where the labels of greatest operational importance are also the rarest ones. In this setting, annotators may be reliable on both classes, unreliable on both classes, majority-class specialists, or minority-class specialists. Existing models only partially address this problem: they either capture class-dependent errors but ignore item difficulty, or they model item difficulty without capturing class-dependent errors. To fill this gap for imbalanced datasets in crowdsourc...
| Authors | Gabriel Singer, Samuel Gruffaz, Olivier Vo Van et al. |
|---|---|
| Primary category | stat.ML |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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