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- Title
ROCS: Receiver Operating Characteristic Surface for Class-Skewed High-Throughput Data.
- Authors
Tianwei Yu
- Abstract
The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of classifiers. In certain situations of high-throughput data analysis, the data is heavily class-skewed, i.e. most features tested belong to the true negative class. In such cases, only a small portion of the ROC curve is relevant in practical terms, rendering the ROC curve and its area under the curve (AUC) insufficient for the purpose of judging classifier performance. Here we define an ROC surface (ROCS) using true positive rate (TPR), false positive rate (FPR), and true discovery rate (TDR). The ROC surface, together with the associated quantities, volume under the surface (VUS) and FDR-controlled area under the ROC curve (FCAUC), provide a useful approach for gauging classifier performance on class-skewed high-throughput data. The implementation as an R package is available at http://userwww.service.emory.edu/∼tyu8/ROCS/
- Subjects
RECEIVER operating characteristic curves; DATA analysis; CURVE fitting; GRAPHIC methods for multivariate analysis; CURVES; GEOMETRY
- Publication
PLoS ONE, 2012, Vol 7, Issue 7, p1
- ISSN
1932-6203
- Publication type
Article
- DOI
10.1371/journal.pone.0040598