A novel accuracy metric for general M-class classification when subclasses are involved
In this paper, we first propose a new accuracy measure proper for ``compound multi-class classification'' with general M main classes where M >= 3, namely ``hypervolume under compound ROC manifold HUMc".
The proposed HUMc evaluates the accuracy of a biomarker appropriately by identifying main classes without requiring the specification of an ordering for marker values of subclasses within each main class.
For confidence interval estimation of ${VUS_C}$, an efficient computing algorithm estimating its empirical estimator is discussed alongside the non-parametric bootstrap confidence interval, and simulation studies are carried out to assess coverage probabilities and interval lengths. Lastly, a real data example concerning cancer is included to illustrate the usage of our proposed method.
To propose a new accuracy metric for compound multi-class classification
Adapt and propose an efficient computing algorithm
Dr. Lili Tian, Department of Biostatistics, State University of New York at Buffalo