Ensemble Learning with Active Example Selection for Imbalanced Biomedical Data Classification

In biomedical data, the imbalanced data problem occurs frequently and causes poor prediction performance for minority classes. It is because the trained classifiers are mostly derived from the majority class. In this paper, we describe an ensemble learning method combined with active example selection to resolve the imbalanced data problem. Our method consists of three key components: 1) an active example selection algorithm to choose informative examples for training the classifier, 2) an ensemble learning method to combine variations of classifiers derived by active example selection, and 3) an incremental learning scheme to speed up the iterative training procedure for active example selection. We evaluate the method on six real-world imbalanced data sets in biomedical domains, showing that the proposed method outperforms both the random under sampling and the ensemble with under sampling methods. Compared to other approaches to solving the imbalanced data problem, our method excels by 0.03-0.15 points in AUC measure.