An Empirical Evaluation of Ranking Measures with Respect to Robustness to Noise
Abstract
Ranking measures play an important role in model evaluation and selection. Using both synthetic and real-world data sets, we investigate how different types and levels of noise affect the area under the ROC curve (AUC), the area under the ROC convex hull, the scored AUC, the Kolmogorov-Smirnov statistic, and the H-measure. In our experiments, the AUC was, overall, the most robust among these measures, thereby reinvigorating it as a reliable metric despite its well-known deficiencies. This paper also introduces a novel ranking measure, which is remarkably robust to noise yet conceptually simple.
Cite
Text
Berrar. "An Empirical Evaluation of Ranking Measures with Respect to Robustness to Noise." Journal of Artificial Intelligence Research, 2014. doi:10.1613/JAIR.4136Markdown
[Berrar. "An Empirical Evaluation of Ranking Measures with Respect to Robustness to Noise." Journal of Artificial Intelligence Research, 2014.](https://mlanthology.org/jair/2014/berrar2014jair-empirical/) doi:10.1613/JAIR.4136BibTeX
@article{berrar2014jair-empirical,
title = {{An Empirical Evaluation of Ranking Measures with Respect to Robustness to Noise}},
author = {Berrar, Daniel P.},
journal = {Journal of Artificial Intelligence Research},
year = {2014},
pages = {241-267},
doi = {10.1613/JAIR.4136},
volume = {49},
url = {https://mlanthology.org/jair/2014/berrar2014jair-empirical/}
}