Robustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selection

arXiv:2603.23318v2 Announce Type: replace Abstract: Among the different possible strategies for evaluating the reliability of individual predictions of classifiers, robustness quantification stands out as a method that evaluates how much uncertainty a classifier could cope with before changing its prediction. However, its applicability is more limited than some of its alternatives, since it requires the use of generative models and restricts the analyses either to specific model architectures or discrete features. In this work, we propose a new robustness metric applicable to any probabilistic

Source: arXiv cs.LG — read the full report at the original publisher.

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