Article reference:

M. Egmont-Petersen, J.L. Talmon, A. Hasman, "Robustness metrics for measuring the influence of additive noise on the performance of statistical classifiers," International Journal of Medical Informatics, Vol. 46, No. 2, pp. 103-112, 1997.


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.

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