We introduce a model class for statistical learning which is based on mixtures of propositional rules. In our mixture model, the weight of a rule is not uniform over the entire instance space. Rather, it depends on the instance at hand. This is motivated by applications in molecular biology, where it is frequently observed that the effect of a particular mutational pattern depends on the geneti...

The area under the ROC curve (AUC) has been advocated as an evaluation criterion for the bipartite ranking problem. We study uniform convergence properties of the AUC; in particular, we derive a distribution-free uniform convergence bound for the AUC which serves to bound the expected accuracy of a learned ranking function in terms of its empirical AUC on the training sequence from which it is ...

This paper describes how to use Receiver Operator Characteristic (ROC) analysis to evaluate radiologists’ performance of interpreting digital mammograms in real-time. We developed an experimental testing system, which implemented a set of clinical lesion-matching rules to prepare raw ROC data. The system can automatically provide detailed evaluations of the performance, such as sensitivity, spe...

Genome-wide association studies in human populations have facilitated the creation of genomic profiles which combine the effects of many associated genetic variants to predict risk of disease. The area under the receiver operator characteristic (ROC) curve is a well established measure for determining the efficacy of tests in correctly classifying diseased and non-diseased individuals. We use q...

The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking to biometric screening to medicine, performance is measured not in terms of the full area under the ROC curve but in terms of the partial area under the ROC curve between two false-positive rates. In this letter, we develop support vec...

In this paper we i n vestigate the use of receiver operating characteristic (ROC) curve f o r the evaluation of machine learning algorithms. In particular, we i n vestigate the use of the area under the ROC curve (A UC) as a measure of classiier performance. The machine learning algorithms used are chosen to be representative of those in common use: two decision trees (C4.5 and Multiscale Class...

quantity is the area under the so-called receiver operating characteristic (ROC) curve, 1 AMS 2000 subject classifications. Primary: 62G99; Secondary: 62H30, 62G20.

Decision lists (or ordered rule sets) have two attractive properties compared to unordered rule sets: they require a simpler classification procedure and they allow for a more compact representation. However, it is an open question what effect these properties have on the area under the ROC curve (AUC). Two ways of forming decision lists are considered in this study: by generating a sequence of...

Assessing the discriminative power of rating systems is an important question to banks and to regulators. In this article we analyze the Cumulative Accuracy Profile (CAP) and the Receiver Operating Characteristic (ROC) which are both commonly used in practice. We give a test-theoretic interpretation for the concavity of the CAP and the ROC curve and demonstrate how this observation can be used ...