Dynamic clustering method for imbalanced learning based on AdaBoost

Our paper aims at learning from imbalance data based on ensemble learning. At the stage, the main solution is to combine under-sampling, oversampling or cost sensitivity learning with ensemble learning. However, these feature space-based methods fail to reflect the transformation of distribution and are usually accompanied with high computational complexity and risk of overfitting. In this paper, we propose a dynamic cluster algorithm based on coefficient of variation (or entropy), which learns the local spatial distribution of data and hierarchically clusters the majority. This algorithm has low complexity and can dynamically adjust the cluster according to the iteration of AdaBoost, adaptively synchronized with changes caused by sample weight changes. Then, we design an index to measure the importance of each cluster. Based on this index, a dynamic sampling algorithm based on maximum weight is proposed. The effectiveness of the sampling algorithm is proved by visual experiments. Finally, we propose a cost-sensitive algorithm based on Bagging, and combine it with the dynamic sampling algorithm to propose a multi-fusion imbalanced ensemble learning algorithm. In experimental research, our algorithms have been validated on three artificial datasets, 22 KEEL datasets and two gene expression cancer datasets, and have shown ideal or better performance than SOTA in terms of AUC, indicating that our algorithms are not only effective imbalance algorithms, but also provide potential for building a reliable biological cyber-physical system.