Imbalanced Classification under Capacity Constraints
Detecting observations from a minority class under severe class imbalance is a central challenge in applications such as fraud detection, medical screening, and industrial quality control. In these settings, each positive prediction triggers a costly follow-up action, an MRI scan, a transaction audit, whose execution is subject to real operational constraints. This paper proposes a formal classification framework under capacity constraints: given a user-defined bound limit $b$ on the proportion of observations that can be labeled as belonging to the minority class, the goal is to find the classifier that maximizes sensitivity on that class. We characterize the optimal classifier under thi...
| Authors | Daniel Fraiman, Ricardo Fraiman |
|---|---|
| Primary category | stat.ML |
| Published | 5 May 2026 |
| Last updated at source | 14 June 2026 |
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