In this paper, inspired by cost-sensitive learning (Ling and Sheng, 2011), we introduce costsensitive regularization to model and exploit the label confusion during model optimization, which can make the training procedure more sensitive to confusing type pairs.
the proposed regularizer reshapes the loss function of model training by penalizing the likelihood of making wrong predictions with a cost-weighted term.