Robustness metrics for measuring the influence of additive noise on the performance of statistical classifiers.

This paper presents a novel quality measure called robustness. The robustness measure quantifies the influence of measurement noise in the attribute values on the credibility of the classification of a case. It is assumed that the type of distribution of the noise-generating process is known. It is not simple to measure the robustness in the general situation where the noise-free distribution of the attributes is unknown. Therefore, two approximations are suggested and compared with the robustness measure based on the noise-free distribution of the attributes. The usefulness of the suggested robustness measure is explored in a simulation experiment.