The paper presents a support vector method for estimating probabilities in a real world problem: the prediction of probability of survival in critically ill patients. The standard procedure with Support Vectors Machines uses Platt’s method to fit a sigmoid that transforms continuous outputs into probabilities. The method proposed here exploits the difference between maximizing the AUC and minim...

A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly ch...

The area under the ROC curve (AUC) is a widely used performance measure in machine learning, and has been widely studied in recent years particularly in the context of bipartite ranking. A dominant theoretical and algorithmic framework for AUC optimization/bipartite ranking has been to reduce the problem to pairwise classification; in particular, it is well known that the AUC regret can be form...

ISSN 2277 5048 | © 2012 Bonfring Abstract--In recent years the Receiver Operating Characteristic (ROC) curves received much attention in medical diagnosis for classifying the subjects into one of the two groups. Many researchers have provided the mathematical formulation of the curve by assuming some specific distribution. Conventionally, much work has been carried out by assuming normal distri...

Many typical applications of object detection operate within a prescribed false-positive range. In this situation the performance of a detector should be assessed on the basis of the area under the ROC curve over that range, rather than over the full curve, as the performance outside the range is irrelevant. This measure is labelled as the partial area under the ROC curve (pAUC). Effective casc...

We investigate rank-based studentized permutation methods for the nonparametric Behrens-Fisher problem, i.e. inference methods for the area under the ROC-curve (AUC). We hereby prove that the studentized permutation distribution of the Brunner-Munzel rank statistic is asymptotically standard normal, even under the alternative. This does not only imply consistency of the corresponding permutatio...

The Area Under the ROC Curve (AUC) metric has achieved a big success in binary classification problems since they measure the performance of classifiers without making any specific assumptions about the class distribution and misclassification costs. This is desirable because the class distribution and misclassification costs may be unknown during training process or even change in environment....

The Receiver Operator Characteristic (ROC) plot allows a classifier to be evaluated and optimised over all possible operating points. The Area Under the ROC (AUC) has become a standard performance evaluation criterion in two-class pattern recognition problems, used to compare different classification algorithms independently of operating points, priors, and costs. Extending the AUC to the multi...

In the literature, there are several criteria for validation of a clustering partition. Those criteria can be external or internal, depending on whether we use prior information about the true class labels or only the data itself. All these criteria assume a fixed number of clusters k and measure the performance of a clustering algorithm for that k. Instead, we propose a measure that provides t...