Correcting Performance Estimation Bias in Imbalanced Classification with Minority Subconcepts
Class-level evaluation can conceal substantial performance disparities across subconcepts within the same class, causing models that perform well on average to fail on specific subpopulations. Prior work has shown that common evaluation measures for imbalanced classification are biased toward larger minority subconcepts and that utility-based reweighting using true subconcept labels can mitigate this bias; however, such labels are rarely available at test time. We introduce a practical utility-weighted evaluation that replaces unavailable subconcept labels with predicted posterior probabilities from a multiclass subconcept model. Evaluation weights are defined as the expected utility unde...
| Authors | Taylor Maxson, Roberto Corizzo, Yaning Wu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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