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/.
Highlights
Receiver Operating Characteristic (ROC) curve is widely used to assess the performance of classifiers in biomedical research [1,2,3]
Only a small corner of the ROC curve is relevant in practical terms, and the area under the curve (AUC) can no longer summarize the effectiveness of the separation
Another heuristic approach was to use a variant of ROC – replacing the false positive rate (FPR) with the false discovery rate (FDR) in the ROC plot [5,6,10]
Summary
Receiver Operating Characteristic (ROC) curve is widely used to assess the performance of classifiers in biomedical research [1,2,3]. The volume under the surface (VUS) of the ROC surface and the FDR-controlled area under the curve (FCAUC), as well as a testing procedure, are defined to help simplify comparison of classifiers in the class-skewed scenarios. Given that each feature belongs to either the true positive or the true negative group, changing the threshold value d will generate a tradeoff between sensitivity and specificity, the ROC curve (Fig. 1).
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