Receiver operating characteristic - Wikipedia, the free encyclopedia

In statistics, a receiver operating characteristic (ROC), or ROC curve, is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the true positive rate against the false positive rate at various threshold settings. (The true-positive rate is also known as sensitivity in biomedical informatics, or recall in machine learning. The false-positive rate is also known as the fall-out and can be calculated as 1 specificity). The ROC curve is thus the sensitivity as a function of fall-out. In general, if the probability distributions for both detection and false alarm are known, the ROC curve can be generated by plotting the cumulative distribution function (area under the probability distribution from to ) of the detection probability in the y-axis versus the cumulative distribution function of the false-alarm probability in x-axis.

[1]  Kent A. Spackman,et al.  Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning , 1989, ML.

[2]  José Hernández-Orallo,et al.  Volume under the ROC Surface for Multi-class Problems , 2003, ECML.

[3]  R. Real,et al.  AUC: a misleading measure of the performance of predictive distribution models , 2008 .

[4]  D. McClish.  Analyzing a Portion of the ROC Curve , 1989, Medical decision making : an international journal of the Society for Medical Decision Making.

[5]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[6]  Jude W. Shavlik,et al.  Uplift Modeling with ROC: An SRL Case Study , 2013, ILP.

[7]  Peter A. Flach,et al.  A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss C` Esar Ferri , 2012 .

[8]  N. Obuchowski.  Receiver operating characteristic curves and their use in radiology. , 2003, Radiology.

[9]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

[10]  M. Pepe.  The Statistical Evaluation of Medical Tests for Classification and Prediction , 2003 .

[11]  Ryan Shaun Joazeiro de Baker,et al.  Case studies in the use of ROC curve analysis for sensor-based estimates in human computer interaction , 2005, Graphics Interface.

[12]  Jinbo Bi,et al.  Regression Error Characteristic Curves , 2003, ICML.

[13]  Stphane Tuffry,et al.  Data Mining and Statistics for Decision Making , 2011 .

[14]  José Hernández-Orallo,et al.  ROC curves for regression , 2013, Pattern Recognit..

[15]  R T Knight,et al.  Recollection and familiarity deficits in amnesia: convergence of remember-know, process dissociation, and receiver operating characteristic data. , 1998, Neuropsychology.

[16]  Blaise Hanczar,et al.  Small-sample precision of ROC-related estimates , 2010, Bioinform..

[17]  M. Glanzer,et al.  Slope of the receiver-operating characteristic in recognition memory. , 1999 .

[18]  Pierre Perruchet,et al.  The exploitation of distributional information in syllable processing , 2004, Journal of Neurolinguistics.

[19]  J. Hanley,et al.  A method of comparing the areas under receiver operating characteristic curves derived from the same cases. , 1983, Radiology.

[20]  Peter A. Flach,et al.  A Coherent Interpretation of AUC as a Measure of Aggregated Classification Performance , 2011, ICML.

[21]  David J. Hand,et al.  A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems , 2001, Machine Learning.

[22]  David M. W. Powers,et al.  The Problem with Kappa , 2012, EACL.

[23]  David J. Hand,et al.  Measuring classifier performance: a coherent alternative to the area under the ROC curve , 2009, Machine Learning.

[24]  David Page,et al.  Support Vector Machines for Differential Prediction , 2014, ECML/PKDD.

[25]  D. Powers,et al.  The problem of Area Under the Curve , 2012, 2012 IEEE International Conference on Information Science and Technology.

[26]  Lori E. Dodd,et al.  Partial AUC Estimation and Regression , 2003, Biometrics.

[27]  Peter A. Flach,et al.  Repairing Concavities in ROC Curves , 2005, IJCAI.

[28]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[29]  Neil A. Macmillan,et al.  Detection theory: A user's guide, 2nd ed. , 2005 .

[30]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[31]  Tom Fawcett,et al.  Robust Classification for Imprecise Environments , 2000, Machine Learning.

[32]  N. Graham,et al.  Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation , 2002 .

[33]  John A. Swets,et al.  Signal Detection Theory and ROC Analysis in Psychology and Diagnostics: Collected Papers , 1996 .

[34]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[35]  D. Mossman.  Three-way ROCs , 1999, Medical decision making : an international journal of the Society for Medical Decision Making.