Immune centroids over-sampling method for multi-class classification

Xusheng Ai, Jian Wu, Victor S. Sheng, Pengpeng Zhao, Yufeng Yao, Zhiming Cui

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings
EditorsTu-Bao Ho, Hiroshi Motoda, Hiroshi Motoda, Ee-Peng Lim, Tru Cao, David Cheung, Zhi-Hua Zhou
Number of pages13
ISBN (Print)9783319180373
StatePublished - 2015
Event19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015 - Ho Chi Minh City, Viet Nam
Duration: May 19 2015May 22 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015
Country/TerritoryViet Nam
CityHo Chi Minh City


  • Imbalanced learning
  • Immune network
  • Over-sampling
  • Resampling
  • Synthetic examples


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