VISAL—A novel learning strategy to address class imbalance

Merge Loss Calculation Method for Highly Imbalanced Data Multiclass Classification

“…For imbalanced datasets, in addition to the RES, another strategy is to assign different weights according to the number of various classes to improve model performance. Sivapuram et al [35] designed a loss function called visually interpretable space adjustment learning (VISAL), which can create more space for generalization of minority class samples by introducing angular and Euclidean margins in the cross-entropy learning strategy. Tian et al [36] proposed a hard class mining loss, which reshapes the CEL by dynamically weighting the loss of each class based on instantaneous recall performance.…”