1997 TheUseoftheAreaundertheROCCurve

• (Bradley, 1997) ⇒ Andrew P. Bradley. (1997). “The Use of the Area under the ROC Curve in the Evaluation of Machine Learning Algorithms.” In: Pattern Recognition Journal, 30(7). doi:10.1016/S0031-3203(96)00142-2

Subject Headings: ROC Analysis, AUC Measure.

Cited By

• http://scholar.google.com/scholar?q=%221997%22+The+Use+of+the+Area+under+the+ROC+Curve+in+the+Evaluation+of+Machine+Learning+Algorithms
• http://dl.acm.org/citation.cfm?id=1746432.1746434&preflayout=flat#citedby

Quotes

Abstract

In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k-Nearest Neighbours, and a Quadratic Discriminant Function) on six eal worl medical diagnostics data sets. We compare and discuss the use of AUC to the more conventional overall accuracy and find that AUC exhibits a number of desirable properties when compared to overall accuracy: increased sensitivity in Analysis of Variance (ANOVA) tests; a standard error that decreased as both AUC and the number of test samples increased; decision threshold independent; and it is invariant to a priori class probabilities. The paper concludes with the recommendation that AUC be used in preference to overall accuracy for single number evaluation of machine learning algorithms.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1997 TheUseoftheAreaundertheROCCurve	Andrew P. Bradley			The Use of the Area under the ROC Curve in the Evaluation of Machine Learning Algorithms				10.1016/S0031-3203(96)00142-2		1997