@inproceedings{e7da5f7463bd4b1c90e258facdc164c1,
title = "Immune centroids over-sampling method for multi-class classification",
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.",
keywords = "Imbalanced learning, Immune network, Over-sampling, Resampling, Synthetic examples",
author = "Xusheng Ai and Jian Wu and Sheng, {Victor S.} and Pengpeng Zhao and Yufeng Yao and Zhiming Cui",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015 ; Conference date: 19-05-2015 Through 22-05-2015",
year = "2015",
doi = "10.1007/978-3-319-18038-0_20",
language = "English",
isbn = "9783319180373",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "251--263",
editor = "Tu-Bao Ho and Hiroshi Motoda and Hiroshi Motoda and Ee-Peng Lim and Tru Cao and David Cheung and Zhi-Hua Zhou",
booktitle = "Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings",
}