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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...
The imbalanced data problem is popular in biomedical classification tasks. Since trained classifiers using imbalanced data are mostly derived from the majority class, their prediction performance is poor for the minority class. In this paper, we propose a novel ensemble learning method based on an active example selection algorithm to resolve the imbalanced data problem. To compensate a possible sub-optimal...
Various real-world biomedical classification tasks suffer from the imbalanced data problem which tends to make the prediction performance of some classes significantly decrease. In this paper, we present an active example selection method with naiumlve Bayes classifier (AESNB) as a solution for the imbalanced data problem. The proposed method starts with a small balanced subset of training examples...
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