Efficient AUC Learning Curve Calculation
BibTeX
@MISC{Bouckaert_efficientauc,
author = {Remco R. Bouckaert},
title = {Efficient AUC Learning Curve Calculation},
year = {}
}
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
Abstract. A learning curve of a performance measure provides a graphical method with many benefits for judging classifier properties. The area under the ROC curve (AUC) is a useful and increasingly popular performance measure. In this paper, we consider the computational aspects of calculating AUC learning curves. A new method is provided for incrementally updating exact AUC curves and for calculating approximate AUC curves for datasets with millions of instances. Both theoretical and empirical justifications are given for the approximation. Variants for incremental exact and approximate AUC curves are provided as well. 1
Citations
| 2215 | Data Mining: Practical machine learning tools and techniques. 2nd edition - Witten, Frank - 2005 |
| 192 | Measuring the accuracy of diagnostic systems - SWETS - 1988 |
| 182 | Signal Detection theory and ROC Analysis - Egan - 1975 |
| 122 | ROC graphs: notes and practical considerations for data mining researchers - Fawcett - 2003 |
| 48 | The geometry of ROC space: Understanding machine learning metrics through ROC isometrics - Flach - 2003 |
| 33 | Improving accuracy and cost of two-class and multi-class probabilistic classifiers using roc curves - Lachiche, Flach - 2003 |
| 9 | The use of ROC curves in test performance evaluation - Schultz - 1986 |
| 2 | Saharon Rosset. Pointwise ROC confidence bounds: An empirical evaluation - Macskassy, Provost - 2005 |
| 1 | Resampling Methods for the Area Under the ROC Curve - Bandos, Rockette, et al. |
