Empowering difficult classes with a similarity-based aggregation in multi-class classification problems
Addressing data complexity for imbalanced data sets: Analysis of SMOTE-based oversampling and evolutionary undersampling
Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems
On the importance of the validation technique for classification with imbalanced datasets: Addressing covariate shift when data is skewed
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes
Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets
