1. Class imbalance learning methods for support vector machines;Batuwita;Neural Computing and Applications,2009
2. The foundations of cost-sensitive learning;Elkan,2001
3. Metacost: A general method for making classifiers cost-sensitive;Domingos,1999
4. SMOTE: Synthetic minority over-sampling technique;Chawla;Journal of Artificial Intelligence Research,2002
5. C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling;Drummond,2003