Area Under the ROC Curve (AUC) is a widely used ranking metric in imbalanced learning due to its insensitivity label distributions. As well-known multiclass extension of AUC, Multiclass AUC (MAUC, a.k.a. M-metric) measures average multiple binary classifiers. In this paper, we argue that simply optimizing MAUC far from enough for multi-classification. More precisely, only focuses on scoring fun...