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Cost-Sensitive Conformal Prediction and Human-in-the-Loop Abstention for Imbalanced High-Stakes Decision Support: A Multi-Domain Benchmark
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- Researchers benchmarked conformal prediction methods for imbalanced datasets in high-stakes domains, showing class-conditional approaches restore minority-class coverage that standard methods miss.
- Why it matters
- Engineers building credit, fraud, healthcare, or safety systems where rare events carry high costs and prediction confidence matters more than accuracy alone.
- Watch out
- Study covers tabular data only; results may not transfer to images, text, or other domains. Real-world deployment still requires careful threshold calibration per use case.
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