Meta-learning for imbalanced data and classification ensemble in binary classification

To conduct binary classification with highly imbalanced data is a very common problem, especially when the examples of interest are relatively rare. In this paper, we proposed the ''Meta Imbalanced Classification Ensemble (MICE)'' algorithm in order to dilute the effect of imbalanced data. In the MICE, the majority group is partitioned based on the transformed features from ''inner product'' to retain the geometric relation between two groups. The empirical results show that the performance of MICE is better than some renowned classification methods in terms of the specificity and the sensitivity.