Previously, several instances of variability in the output of pattern classifiers that have the same Receiver Operating Characteristic (ROC) curve have been observed. We present a theoretical framework for understanding some sources of this variability, which result in classifiers with monotonically related outputs. We restrict our analysis to pattern classifiers that discriminate between two linearly separable classes. We show that variability in the output of pattern classifiers can arise due to differences in the functional mappings between their inputs and outputs. We further identify some practical situations wherein such variability in the output of such pattern classifiers arises. These include situations in which there are differences in (a) the datasets employed for training and evaluation of classifiers, (b) the a priori probabilities of the two classes, or (c) the stochastic processes employed for training the different pattern classifiers. Previously, we proposed a technique based on the matching of the histograms of differently distributed classifier output to reduce the variability in their diagnostic performance and their output values. Here, we prove theoretically and demonstrate empirically on simulated data, that for monotonically related classifier outputs, this technique successfully learns the true monotonic transformation function that exists between different pattern classifier outputs.

Date Published: 12 March 2009
PDF: 11 pages
Proc. SPIE 7263, Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment, 72630Y (12 March 2009);

Published in SPIE Proceedings Vol. 7263:
Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment
Berkman Sahiner; David J. Manning, Editor(s)