Stochastic Proximal AUC maximization algorithm
The files above contain code for the Stochastic Proximal AUC maximization algorithm of Y. Lei et al. This is a machine learning algorithm for two class imbalanced datasets that fits a linear predictive model to the data, with the intention of maximizing the area under the precision-recall curve.
There is also a project summary comparing the results of this algorithm to a random forest algorithm, with and without SMOTE and cost-sensitive approach.