NEATER: Filtering of Over-sampled Data Using Non-cooperative Game Theory

We present a method for the filtering of over-sampled data using non-cooperative game theory (NEATER) to address the imbalanced data problem using game theory. Specifically, the problem is formulated as a non-cooperative game where all the data are players and the goal is to uniformly and consistently label all of the synthetic data created by any over-sampling technique. We present extensive experimental results which demonstrate the advantages of our method.