The Area Under the Receiver Operating Characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in supervised learning domain. Due its compelling features, it has been employed number of studies evaluate and compare different classifiers. In this work, we explore AUC unsupervised domain, more specifically, context cluster analysis. particular, elaborate on use an inte...

The mathematical relationship between the expectedconfusion metric and the area under a receiver operating characteristic (ROC) curve is derived. Given a limited database of subjects and an identification technique that generates a feature vector per subject, expected confusion is used to predict how well the feature vector will filter identity in a larger population. Related is the area under ...

The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of classifiers. In certain situations of high-throughput data analysis, the data is heavily class-skewed, i.e. most features tested belong to the true negative class. In such cases, only a small portion of the ROC curve is relevant in practical terms, rendering the ROC curve and its area under the cu...