Receiver operating characteristics of perceptrons: Influence of sample size and prevalence

Ansgar Freking, Michael Biehl, Christian Braun, Wolfgang Kinzel, Malte Meesmann
1999 Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics  
In many practical classification problems it is important to distinguish false positive from false negative results when evaluating the performance of the classifier. This is of particular importance for medical diagnostic tests. In this context, receiver operating characteristic ͑ROC͒ curves have become a standard tool. Here we apply this concept to characterize the performance of a simple neural network. Investigating the binary classification of a perceptron we calculate analytically the
more » ... nalytically the shape of the corresponding ROC curves. The influence of the size of the training set and the prevalence of the quality considered are studied by means of a statistical-mechanics analysis. ͓S1063-651X͑99͒06911-1͔
doi:10.1103/physreve.60.5926 pmid:11970494 fatcat:qgi2qfrx3jg7vbwct5fdeomcky