Aligning boundary in kernel space for learning imbalanced dataset

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 nontarget 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.