The receiver operating characteristic (ROC) has emerged as the gold standard for assessing and comparing the performance of classifiers in a wide range of disciplines including the life sciences. ROC curves are frequently summarized in a single scalar, the area under the curve (AUC). This article discusses the caveats and pitfalls of ROC analysis in clinical microarray research, particularly in...

There are different approaches in the literature for the assessment of steganographic algorithms and steganalytic attacks. In the early papers it was considered sufficient to show the existence of an effect for one or a few examples only. The more the area of steganography evolved, the more diverse became the goals and the harder to measure the improvements. Many branches of science are facing ...

This paper applies receiver operating characteristics (ROC) analysis to M3 Competition, micro monthly time series for one-month-ahead forecasts. Using the partial area under the curve (PAUC) criterion as a forecast accuracy measure and paired-comparison testing via bootstrapping, we find that complex methods (AutomatANN, Flores-Pearce2, Forecast ProSmart FCS, and Theta) perform best for forecas...

UNLABELLED Receiver operating characteristic (ROC) analysis is usually applied in bioinformatics to evaluate the abilities of biological markers to differentiate between the presence or absence of a disease. It includes the derivation of the useful scalar performance measure area under the ROC curve for binary classification tasks. As real applications often deal with more than two classes, mul...