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 discriminative classifier and any type of features. We demonstrate that this new metric is capable of distinguishing between reliable and unreliable predictions, and use this observation to develop new strategies for dynamic classifier selection.

翻译：在评估分类器单个预测可靠性的各种策略中，鲁棒性量化作为一种评估分类器在改变其预测之前能够应对多少不确定性的方法而脱颖而出。然而，其适用性比某些替代方法更为有限，因为它需要使用生成模型，并将分析限制在特定的模型架构或离散特征上。在这项工作中，我们提出了一种新的鲁棒性度量，适用于任何概率判别式分类器和任何类型的特征。我们证明，这种新度量能够区分可靠和不可靠的预测，并利用这一观察结果开发了动态分类器选择的新策略。