GIR-based ensemble sampling approaches for imbalanced learning

This paper presents two adaptive ensemble sampling approaches for imbalanced learning: one is the undersampling-based approach, and the other one is the oversampling-based approach, with the objectives of bias reduction and adaptive learning. Both of these two approaches are based on a novel class imbalance metric, termed generalized imbalance ratio (GIR), instead of the conventional sample size ratio. Specifically, these two sampling-based approaches adaptively split the imbalanced learning problem into multiple balanced learning subproblems in a probabilistic way, which forces the classifiers trained in the subproblems focus on those difficult to learn samples. In each subproblem, several weak classifiers are trained in a boosting manner. A final stronger classifier is further built by combining all these weak classifiers in a bagging manner. Extensive experiments are conducted on real-life UCI imbalanced data sets to evaluate the performance of the proposed methods. The superior performance demonstrates the effectiveness of the proposed methods and indicates wide potential applications in data mining.