The area of imbalanced datasets is still relatively new, and it is known that the use of overall accuracy is not an appropriate evaluation measure for imbalanced datasets, because...
A new framework is proposed for comparing evaluation metrics in classification applications with imbalanced datasets (i.e., the probability of one class vastly exceeds others). Fo...
Mehrdad Fatourechi, Rabab K. Ward, Steven G. Mason...
Real life datasets often suffer from the problem of class imbalance, which thwarts supervised learning process. In such data sets examples of positive (minority) class are signific...
The class imbalance problem (when one of the classes has much less samples than the others) is of great importance in machine learning, because it corresponds to many critical app...
—Ordinal regression (OR – also known as ordinal classification) has received increasing attention in recent times, due to its importance in IR applications such as learning to...
Stefano Baccianella, Andrea Esuli, Fabrizio Sebast...