The meaning and use of the area under a receiver operating characteristic (ROC) curve.

By James A. Hanley, B. McNeil. 1982

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Citations in the corpus

Machine Learning that Matters (2012, background): “The common practice of reporting the area under the curve (AUC) (Hanley & McNeil, 1982) has several drawbacks, including summarizing performance over all possible regimes even if they are unlikely ever to be used (e.g., extremely high false positive rates), and weighting false positives and false…”
SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, background): “The difference between two AUC ROC values can be qualiﬁed as statistically signiﬁcant according to a P value from a bivariate statistical test [46], where a smaller P value indicates a higher likelihood of the difference being signiﬁcant.”
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication (2018, methodology): “(31) The resolvent (30) can be obtained by solving a one dimensional equation for learning problems like Logistic Regression, and admits closed form solution for problems like least square and ℓ2-relaxed AUC maximization.”
Detecting Cancer Metastases on Gigapixel Pathology Images (2017, neutral): “The first metric, the area under receiver operating characteristic, (Area Under ROC, AUC) [10] evaluates slide-level classification.”
Open-Ended Learning Leads to Generally Capable Agents (2021, methodology): “The use of curves of normalised score with respect to percentiles to visualise and characterise performance is inspired by ROC curves (Hanley and McNeil, 1982).”
RaptorX-Property: a web server for protein structure property prediction (2016, methodology): “To deal with the imbalanced distribution of some property label, such as 8-state secondary structure and order / disorder, we train DeepCNF by maximizing area under the ROC curve (AUC), which is a good measure for class-imbalanced data (16).”
Robust Classification for Imprecise Environments (2000, methodology): “The AUC is equivalent to the probability that a randomly chosen positive instance will be rated higher than a negative instance, and thereby is also estimated by the Wilcoxon test of ranks (Hanley & McNeil, 1982).”
Image-based multivariate profiling of drug responses from single cells (2007, methodology): “The area under the receiver operating characteristic curve (AUC) was estimated using the trapezoidal integration, and is equal to the Wilcoxon statisti”
Do We Know Who Will Drop Out?: A Review of the Predictors of Dropping out of High School: Precision, Sensitivity, and Specificity (2013, methodology): “Following the recommendations of signal detection theory (Fawcett, 2004; Hanley & McNeil, 1982; Swets, 1988; Vivo & Franco, 2008; Zwieg & Campbell, 1993), the ROC plot in Figure 2 plots the true-positive proportion against the false-positive proportion.”
Identifying Patients at High Risk for Neutropenic Complications During Chemotherapy for Metastatic Breast Cancer With Doxorubicin or Pegylated Liposomal Doxorubicin: The Development of a Prediction Model (2008, methodology): “The predictive accuracy of the final model and risk scoring system was then determined by measuring the specificity, sensitivity and area under the receiver operating characteristic (ROC) curve.(22,23) Discrimination refers to the ability of a diagnostic test or predictive tool to accurately identif”
More Accurate Student Modeling through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing (2008, methodology): “We use A' (the probability that the model can distinguish a correct response from an incorrect response [8]) and correlation as the measures of model accuracy.”