EUS SVMs: ensemble of under-sampled SVMs for data imbalance problems

Authors:
Pilsung Kang;Sungzoon Cho
Affiliations:
Seoul National University, Seoul, Korea;Seoul National University, Seoul, Korea
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

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Abstract

Data imbalance occurs when the number of patterns from a class is much larger than that from the other class. It often degenerates the classification performance. In this paper, we propose an Ensemble of Under-Sampled SVMs or EUS SVMs. We applied the proposed method to two synthetic and six real data sets and we found that it outperformed other methods, especially when the number of patterns belonging to the minority class is very small.