Paper: On the quality of neural net classifiers

Authors:
Michael Egmont-Petersen;Jan L. Talmon;Jytte Brender;Peter McNair
Affiliations:
Dept. of Medical Informatics, University of Limburg, PO Box 616, 6200 MD Maastricht, The Netherlands;Dept. of Medical Informatics, University of Limburg, PO Box 616, 6200 MD Maastricht, The Netherlands;Medical Informatics Laboratory Aps, Stengaards Allé 33d, DK-2800 Lyngby, Denmark;Dept. of Clinical Chemistry, University of Copenhagen, Hvidovre Hospital, Kettegaard Allé 30, DK-2650 Hvidovre, Denmark
Venue:
Artificial Intelligence in Medicine
Year:
1994

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Abstract

This paper describes several concepts and metrics that may be used to assess various aspects of the quality of neural net classifiers. Each concept describes a property that may be taken into account by both designers and users of neural net classifiers when assessing their utility. Besides metrics for assessment of the correctness of classifiers we also introduce metrics that address certain aspects of the misclassifications. We show the applicability of the introduced quality concepts for selection among several neural net classifiers in the domain of thyroid disorders.