Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A note on the utility of incremental learning
AI Communications
Experimental perspectives on learning from imbalanced data
Proceedings of the 24th international conference on Machine learning
Active learning for class imbalance problem
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning on the border: active learning in imbalanced data classification
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
BIBE '09 Proceedings of the 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Operations for learning with graphical models
Journal of Artificial Intelligence Research
EUS SVMs: ensemble of under-sampled SVMs for data imbalance problems
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Knowledge discovery approach to automated cardiac SPECT diagnosis
Artificial Intelligence in Medicine
A hierarchical neural network architecture for classification
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Multi-label ensemble based on variable pairwise constraint projection
Information Sciences: an International Journal
A multilayered ensemble architecture for the classification of masses in digital mammograms
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Neurocomputing
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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 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.