Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation

By Simon Mason, N. Graham. 2002

902 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/603151c5fd127fbdaf3b6f952f68f79823da87f6

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In Global and Planetary Change

In Management Science and Operations Research

Citations in the corpus

A general framework for determining cutoff values to select pollen analogs with dissimilarity metrics in the modern analog technique (2004, methodology): “... is identical to ‘Receiver Operating Characteristic’ (ROC) analysis, ¢rst developed for examination of signal detection characteristics of radar systems and widely employed in a number of other ¢elds: e.g., in the evaluation of medical and psychological laboratory tests, and the evaluation of wea”
Sampling Uncertainty and Confidence Intervals for the Brier Score and Brier Skill Score (2008, background): “…such as the probability of detection or hit rate (Kane and Brown 2000; Jolliffe 2007), odds ratio (Stephenson 2000), false alarm rate and skill scores (Thornes and Stephenson 2001), the correlation coefficient (Jolliffe 2007), and relative operating characteristics (Mason and Graham 2002).”
Thresholded ConvNet ensembles: neural networks for technical forecasting (2018, methodology): “…curve (AUC) of each model’s receiver operating characteristic curve (ROC), and exploit an equivalence between the AUC and Mann-Whitney-Wilcoxon test statistic U (Mason and Graham, 2002): AUC = U nPnN (4) where nP and nN are the number of positive and negative returns in the test set, respectively.”
Within-subject template estimation for unbiased longitudinal image analysis (2012, methodology): “Note that the Mann-Whitney-U test is closely related to the area under the Receiver Operator Characteristic (ROC) (Mason and Graham, 2002).”
ASH structure alignment package: Sensitivity and selectivity in domain classification (2007, background): “Plotting the true positive ratio against the false positive ratio yields the receiver operating characteristic (ROC) curve, the area under which can be interpreted as "the probability of making a correct choice" given two observations, one true and one false[5].”
Psychometric Properties of the Dutch Version of the Eating Disorder Inventory–3 (2013, neutral): “AUC scores of .50 indicate no distinctive ability of the subscale to distinguish between both groups, while scores of .80 indicate that with this subscale, 80% of the subjects could be correctly classified (Fawcett, 2006; Mason & Graham, 2002).”
UCell: Robust and scalable single-cell gene signature scoring (2021, methodology): “We note that the U statistic is closely related to the area-underthe-curve (AUC) statistic for ROC curves [8], therefore we expect UCell scores to correlate with methods based on AUC scores such as AUCell [6].”
CNNs for automatic glaucoma assessment using fundus images: an extensive validation (2019, neutral): “Once the models were fine-tuned, 46 ms are needed to assign a glaucoma probability for each retinal image.”
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer (2017, neutral): “Training (n = 270 images) RUMC 54 16 100 35 35 2 (1-20) 170”