Additional resources are available here:

• Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). SMOTE: synthetic minority oversampling technique. Journal of artificial intelligence research, 16: 321–357.
• Chen, Chao, Andy Liaw, and Leo Breiman. “Using random forest to learn imbalanced data.” University of California, Berkeley 110 (2004): 1-12.
• Wilson, D. L. (1972). Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics, (3): 408–421.
• Pozzolo, et al., Calibrating Probability with Undersampling for Unbalanced Classification (2015), 2015 IEEE Symposium Series on Computational Intelligence.