The so-called “boosting” principle was introduced by Schapire and Freund in the 1990s in relation to weak learners in the Probably Approximately Correct computational learning framework. Another practice that has developed in recent years consists in assessing the quality of evolutionary or genetic classifiers with Receiver Operating Characteristics (ROC) curves. Following the RankBoost algorit...

We study generalization properties of the area under the ROC curve (AUC), a quantity that has been advocated as an evaluation criterion for the bipartite ranking problem. The AUC is a different term than the error rate used for evaluation in classification problems; consequently, existing generalization bounds for the classification error rate cannot be used to draw conclusions about the AUC. I...

Performance evaluation of any classification method is fundamental to its acceptance in practice. Evaluation should consider the dependence of a classifier’s accuracy on relevant covariates in addition to its overall accuracy. When developing a classifier with a continuous output that allocates units into one of two groups, receiver operating characteristic (ROC) curve analysis is appropriate. ...

We study generalization properties of the area under an ROC curve (AUC), a quantity that has been advocated as an evaluation criterion for bipartite ranking problems. The AUC is a different and more complex term than the error rate used for evaluation in classification problems; consequently, existing generalization bounds for the classification error rate cannot be used to draw conclusions abo...

ROC curve analysis is used widely in medicine as a method for evaluating the performance of diagnostic tests (3,5,6,10), but has been used recently in many agricultural applications (2,4,5,11,12). The ROC curve provides information regarding how often a test’s predictions are correct, and provides a graphical method for evaluating and discriminating between different diagnostic tests or modific...

For a continuous-scale diagnostic test, the most commonly used summary index of the receiver operating characteristic curve (ROC) is the area under the curve (AUC) that measures the accuracy of the diagnostic test. In this article, we propose an empirical likelihood (EL) approach for the inference on the AUC. First we define an EL ratio for the AUC and show that its limiting distribution is a s...

ROC analysis is increasingly being recognised as an important tool for evaluation and comparison of classifiers when the operating characteristics (i.e. class distribution and cost parameters) are not known at training time. Usually, each classifier is characterised by its estimated true and false positive rates and is represented by a single point in the ROC diagram. In this paper, we show how...

Influence of Imprecision on ROC Curve Analysis for Cardiac Markers, Peter Kupchak, Alan H.B. Wu, Farooq Ghani, L. Kristen Newby, E. Magnus Ohman, and Robert H. Christenson ( Nanogen, Point of Care Diagnostics Division, Toronto, Ontario, Canada; 2 Department of Laboratory Medicine, San Francisco General Hospital/University of California, San Francisco, CA; 3 Bayer Healthcare Corporation, Tarryto...

Background and Objectives: Gestational Diabetes Mellitus (GDM) is the most common metabolic disorder in pregnancy. In case of early detection, some of its complications can be prevented. The aim of this study was to investigate early prediction of GDM by logistic regression (LR), discriminant analysis (DA), decision tree (DT) and perceptron artificial neural network (ANN) and to compare these m...

Abstract Background: Recent studies have suggested that oxidative stress (OS) may have a contribution in the pathogenesis of endometriosis. However, the results of previous studies regarding OS in endometriosis are controversial. The objective of this study was to compare the serum levels of OS markers in endometriosis versus the control group. Methods: This case-control study was carried out...