The incident/dynamic time-dependent AUC (Area Under the ROC Curve) is an appealing measure to express discriminative value of a dynamic survival model over time. However, estimation this not straightforward. Four recently proposed approaches are studied. In extensive simulation study, head-to-head comparison between these four methods made. algorithms some extended. Results illustrated with mot...

A learning curve of a performance measure provides a graphical method with many benefits for judging classifier properties. The area under the ROC curve (AUC) is a useful and increasingly popular performance measure. In this paper, we consider the computational aspects of calculating AUC learning curves. A new method is provided for incrementally updating exact AUC curves and for calculating ap...

The area under the ROC curve (AUC) is a natural performance measure when the goal is to find a discriminative decision function. We present a rigorous derivation of an AUC maximizing Support Vector Machine; its optimization criterion is composed of a convex bound on the AUC and a margin term. The number of constraints in the optimization problem grows quadratically in the number of examples. We...

Abstract Measuring the performance of a classifier is vital task in machine learning. The running time an algorithm that computes measure plays very small role offline setting, for example, when being developed by researcher. However, becomes more crucial if our goal to monitor over time. In this paper we study three algorithms maintaining two measures. first maintains area under ROC curve (AUC...