Performances evaluation of image processing intermediate results in video based surveillance systems is extremely important due to the variety of approaches to this task. In this paper, an approach based on the use of Receiver Operating Characteristics (ROC) curves in order to evaluate the performances of a vision complex system for surveillance purposes is presented. ROC curves have already be...

Consider the ROC surface which is a generalization of the ROC curve for three−class diagnostic problems. In this work, we propose five criteria for the three−class ROC surface by extending the Youden index, the sum of sensitivity and specificity, the maximum vertical distance, the amended closest-to-(0,1) and the true rate. It may be concluded that these five criteria can be expressed as a func...

Abstract Receiver operating characteristic (ROC) curves are used ubiquitously to evaluate scores, features, covariates or markers as potential predictors in binary problems. We characterize ROC from a probabilistic perspective and establish an equivalence between cumulative distribution functions (CDFs). These results support subtle shift of paradigms the statistical modelling curves, which we ...

OBJECTIVE Pediatric early warning scores (PEWS) are being advocated for use in the emergency department (ED). The goal of this study was to compare the validity of different PEWS in a pediatric ED. METHODS Ten different PEWS were evaluated in a large prospective cohort. We included children aged <16 years who had presented to the ED of a university hospital in The Netherlands (2009-2012). The...

Receiver operating characteristic (ROC) analysis is a fundamental tool used for the evaluation and comparison of diagnostic systems that provides estimates of the combinations of sensitivity and specificity that can be achieved with a given technique. Along with critical considerations of practical limitations, such as throughput and time to availability of results, ROC analyses can be applied ...

The use of incremental reduced error pruning for maximizing the area under the ROC curve (AUC) instead of accuracy is investigated. A commonly used accuracy-based exclusion criterion is shown to include rules that result in concave ROC curves as well as to exclude rules that result in convex ROC curves. A previously proposed exclusion criterion for unordered rule sets, based on the lift, is on ...

Receiver operating characteristic (ROC) curves and in particular the area under the curve (AUC), are widely used to examine the effectiveness of diagnostic markers. Diagnostic markers and their corresponding ROC curves can be strongly influenced by covariate variables. When several diagnostic markers are available, they can be combined by a best linear combination such that the area under the R...

In this paper we study techniques for generating and evaluating confidence bands on ROC curves. ROC curve evaluation is rapidly becoming a commonly used evaluation metric in machine learning, although evaluating ROC curves has thus far been limited to studying the area under the curve (AUC) or generation of one-dimensional confidence intervals by freezing one variable—the false-positive rate, o...