RAMOBoost: ranked minority oversampling in boosting
IEEE Transactions on Neural Networks
Combining integrated sampling with SVM ensembles for learning from imbalanced datasets
Information Processing and Management: an International Journal
Boosting prediction accuracy on imbalanced datasets with SVM ensembles
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Adjusted F-measure and kernel scaling for imbalanced data learning
Information Sciences: an International Journal
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An imbalanced training dataset poses serious problem for many real-world supervised learning tasks. In this paper, we propose a kernel-boundary-alignment algorithm, which considers training-data imbalance as prior information to augment SVMs to improve class-prediction accuracy. Using a simple example, we first show that SVMs can suffer from high incidences of false negatives when the training instances of the target class are heavily outnumbered by the training instances of a non-target class. The remedy we propose is to adjust the class boundary by modifying the kernel matrix, according to the imbalanced data distribution. Through theoretical analysis backed by empirical study, we show that our kernel-boundary-alignment algorithm works effectively on several datasets.