@inproceedings{32f18427c09846ea8367189e03fe7d98,
title = "On the noise resilience of ranking measures",
abstract = "Performance measures play a pivotal role in the evaluation and selection of machine learning models for a wide range of applications. Using both synthetic and real-world data sets, we investigated the resilience to noise of various ranking measures. Our experiments revealed that the area under the ROC curve (AUC) and a related measure, the truncated average Kolmogorov-Smirnov statistic (taKS), can reliably discriminate between models with truly different performance under various types and levels of noise. With increasing class skew, however, the H-measure and estimators of the area under the precision-recall curve become preferable measures. Because of its simple graphical interpretation and robustness, the lower trapezoid estimator of the area under the precision-recall curve is recommended for highly imbalanced data sets.",
keywords = "AUC, Classification, H-measure, Noise, Precision-recall curve, Ranking, Robustness, ROC curve, TaKS",
author = "Daniel Berrar",
year = "2016",
doi = "10.1007/978-3-319-46672-9_6",
language = "English",
isbn = "9783319466712",
volume = "9948 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "47--55",
booktitle = "Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings",
note = "23rd International Conference on Neural Information Processing, ICONIP 2016 ; Conference date: 16-10-2016 Through 21-10-2016",
}