Enrich the data density of cluster for imbalanced learning using immune representatives
Xusheng Ai, Jian Wu, Zhiming Cui, Xuefeng Xian, Yufeng Yao · 2016
To deal with between-class and within-class imbalances, a novel over-sampling method, shaped-based oversampling (SBO) is proposed. It reduces the dependency of parameter setting of CURE by generating the variable-length representatives, which represents data architecture. Meanwhile, out method discriminates faked clusters and generates immune representatives in small disjuncts. As immune representatives are not copies of original examples, overfitting is also alleviated. Our experimental results also shows that our proposed over-sampling method SBO can achieve better performance than other renowned re-sampling methods.