Area under an ROC curve plays an important role in estimating discrimination performance – a well-known theorem by Green (1964) states that ROC area equals the percentage of correct in two-alternative forcedchoice setting. When only single data point is available, the upper and lower bound of discrimination performance can be constructed based on the maximum and minimum area of legitimate ROC c...

RATIONAL AND OBJECTIVES It is common to administer the same diagnostic test more than once to the same set of patients. The purpose of this study was to develop two statistical methods for estimating and comparing correlated receiver operating characteristic (ROC) curves for data derived from repeated diagnostic tests. MATERIAL AND METHODS Parametric and semiparametric transformation models w...

Purpose: The aim was studying the discriminability by ROC curves and gain charts for simple fixed combining of constituent classifiers, for asthma severity diagnosis, and also for bagging and boosting. Material and methods: ROC shows a performance over a range of relative costs and probabilities a priori. Area under ROC curve (AUC) is the measure of separability of two probability distributions...

Evaluating classifier performance with ROC curves is popular in the machine learning community. To date, the only method to assess confidence of ROC curves is to construct ROC bands. In the case of severe class imbalance, ROC bands become unreliable. We propose a generic framework for classifier evaluation to identify the confident segment of an ROC curve. Confidence is measured by Tango’s 95%-...

The receiver operating characteristic (ROC) curve is widely used for diagnosing as well as for judging the discrimination ability of different statistical models. Although theories about ROC curves have been established and computation methods and computer software are available for cross-sectional design, limited research for estimating ROC curves and their summary statistics has been done for...

Receiver operating characteristic (ROC) analysis is the commonly accepted method for comparing diagnostic imaging systems. In general, ROC studies are designed in such a way that multiple readers read the same images and each image is presented by means of two different imaging systems. Statistical methods for the comparison of the ROC curves from one reader have been developed, but extension o...

Evaluating classifier performance with ROC curves is popular in the machine learning community. To date, the only method to assess confidence of ROC curves is to construct ROC bands. In the case of severe class imbalance with few instances of the minority class, ROC bands become unreliable. We propose a generic framework for classifier evaluation to identify a segment of an ROC curve in which m...