Optimizing Classifers for Imbalanced Training Sets

Following recent results [9, 8] showing the importance of the fatshattering dimensionin explaining the beneficial effect of a large margin on generalization performance, the current paper investigates theimplications of these results for the case of imbalanced datasets and develops two approaches to setting the threshold. The approaches are incorporated into ThetaBoost, a boosting algorithm fordealing with unequal loss functions. The performance of ThetaBoost and the two approaches are tested experimentally.