Classification for imbalanced dataset based on biased empirical feature mapping

It is shown that an imbalanced datasets can pose serious problems to many real-world classification tasks when support vector machines is used as the learning machine. To solve this problem, we propose a modified method based on biased empirical feature mapping. In the new method, biased discriminant analysis was applied to make all majority samples far away from center of minority samples in empirical feature space, so that generalization ability of the classifier for minority samples can be improved. Through theoretical analysis and empirical study on synthetic datasets and UCI datasets, we show that our method augments the classification accuracy rate effectively.