Entropy-based optimisation for binary detection networks

Abstract

This contribution deals with binary detection networks optimisation using an entropy-based criterion. The optimisation of a detection elementary component consists in applying a variable threshold on the likelihood ratio, which depends on a posteriori probabilities. A gradient algorithm is proposed to find this threshold. The optimization results of the detection elementary component using entropy and Bayes' criteria are compared: the proposed approach has a very interesting property of robustness with respect to rare events, and with respect to events for which a priori probabilities are uncertain. In particular, the obtained ROC curve does not recede from the ideal point.