Optimizing classifers for imbalanced training sets

Following recent results [9, 8] showing the importance of the fat (cid: 173) shattering dimension in explaining the beneficial effect of a large margin on generalization performance, the current paper investi (cid: 173) gates the implications 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 al (cid: 173) gorithm for dealing with unequal loss functions. The performance of ThetaBoost and the two approaches are tested experimentally.