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A Comparison of Dierent ROC Measures for Ordinal Regression

Ordinal regression learning has characteristics of both multi-class classification and metric regression because labels take ordered, discrete values. In applications of ordinal regression, the misclassification cost among the classes often diers and with dierent misclassification costs the common performance measures are not appropriate. Therefore we extend ROC analysis principles to ordinal regression. We derive an exact expression for the volume under the ROC surface (VUS) spanned by the true positive rates for each class and show its interpretation as the probability that a randomly drawn sequence with one object of each class is correctly ranked. Because the computation of V US has a huge time complexity, we also propose three approximations to this measure. Furthermore, the properties of VUS and its relationship with the approximations are analyzed by simulation. The results demonstrate that optimizing various measures will lead to dierent models.