… AUC is a better measure (defined precisely) than accuracy. We then reevaluate well-established
claims in machine learning based on accuracy using AUC … decision trees in AUC. The …

… (ROC) curve, known as the AUC, is currently considered to be the … We do not recommend
using AUC for five reasons: (1) it … the rate of well‐predicted absences and the AUC scores. …

… than accuracy, we substitute f by AUC and g by accuracy in the definition above. To simplify
our notation, we will use AUC to represent AUC values, and acc for accuracy. The domain Ψ …

… AUC, has been proposed as an alternative single-number measure for evaluating learning
algorithms. In this paper, we prove that AUC … proof to establish that AUC is indeed statistically …

In this paper we show an efficient method for inducing classifiers that directly optimize the
area under the ROC curve. Recently, AUC gained importance in the classification community …

… for AUC and AUC estimators in Section 2. We give definitions for both AUC and partial AUC
and … In Section 3, we review different objective functions for AUC maximization and mainly dis…

… tests for the difference between AUC scores of two algorithms on … AUC to empirical
misclassification error when the prediction goal is to minimize future error rate. We show that the …

… the AUC and the error rate, including the first exact expression of the expected value and
the variance of the AUC for a fixed error rate. Our results show that the average AUC is …

Progress in analytical ultracentrifugation (AUC) has been hindered by obstructions to hardware
innovation and by software incompatibility. In this paper, we announce and outline the …

AUC is an important performance measure and many algorithms have been devoted to AUC
optimization, mostly by minimizing a surrogate convex loss on a training data set. In this …