Abstract
To improve the classification performance of imbalanced learning, a novel over-sampling method, Global Immune Centroids Over-Sampling (Global-IC) based on an immune network, is proposed. Global-IC generates a set of representative immune centroids to broaden the decision regions of small class spaces. The representative immune centroids are regarded as synthetic examples in order to resolve the imbalance problem. We utilize an artificial immune network to generate synthetic examples on clusters with high data densities. This approach addresses the problem of synthetic minority oversampling techniques, which lacks of the reflection on groups of training examples. Our comprehensive experimental results show that Global-IC can achieve better performance than renowned multi-class resampling methods.
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Ai, X., Wu, J., Sheng, V.S., Zhao, P., Yao, Y., Cui, Z. (2015). Immune Centroids Over-Sampling Method for Multi-Class Classification. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9077. Springer, Cham. https://doi.org/10.1007/978-3-319-18038-0_20
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DOI: https://doi.org/10.1007/978-3-319-18038-0_20
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