Utilizing Class Separation Distance for the Evaluation of Corruption Robustness of Machine Learning Classifiers. (arXiv:2206.13405v1 [cs.LG] CROSS LISTED)

Robustness is a fundamental pillar of Machine Learning (ML) classifiers,
substantially determining their reliability. Methods for assessing classifier
robustness are therefore essential. In this work, we address the challenge of
evaluating corruption robustness in a way that allows comparability and
interpretability on a given dataset. We propose a test data augmentation method
that uses a robustness distance $\epsilon$ derived from the datasets minimal
class separation distance. The resulting MSCR (mean statistical corruption
robustness) metric allows a dataset-specific comparison of different …

arxiv classifiers evaluation learning lg machine machine learning robustness