title: Robustness metrics for measuring the influence of additive noise on the performance of statistical classifiers.
abstract: 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.
authors.affiliation: Department of Biophysics, Maastricht University, The Netherlands. michael.egmont@bf.unimaas.nl
authors.forename: M
authors.initials: M
authors.lastname: Egmont-Petersen
authors.forename: J L
authors.initials: JL
authors.lastname: Talmon
authors.forename: A
authors.initials: A
authors.lastname: Hasman
mesh_terms: D001499:Bayes Theorem; D015233:Models, Statistical; D011786:Quality Control; D015203:Reproducibility of Results