A dynamic over-sampling procedure based on sensitivity for multi-class problems
Training cost-sensitive neural networks with methods addressing the class imbalance problem
A massively parallel architecture for a self-organizing neural pattern recognition machine
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
Asymptotic Properties of Nearest Neighbor Rules Using Edited Data
A study of the behavior of several methods for balancing machine learning training data