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arXiv Machine Learning By Zheng Cui, Daniel Zhuoyu Long, Jin Qi, Chen Yang, Ruohan Zhan

Fragility-aware Classification for Risk Characterization and Robust Learning

Read the original on arXiv Machine Learning →

The paper introduces the Fragility Index (FI), a new performance metric that measures the tail risk of confident misclassifications in classification models. FI is framed within a robust satisficing framework to assess classifier sensitivity under distributional uncertainty, and the authors develop a surrogate-based training method with provable bounds linking training fragility to ranking-error FI. The approach is extended to common loss functions and includes an FI-based regularizer for deep neural networks, with empirical studies on medical diagnosis tasks showing that FI reveals risk not captured by accuracy or AUC and leads to models with lower operational costs in a human–AI diagnostic workflow.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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