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.
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Kang, P., Cho, S. (2006). EUS SVMs: Ensemble of Under-Sampled SVMs for Data Imbalance Problems. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_93
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DOI: https://doi.org/10.1007/11893028_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
Online ISBN: 978-3-540-46480-8
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