Hybrid Classifier Ensemble for Imbalanced Data

Kaixiang Yang, Zhiwen Yu, Xin Wen, Wenming Cao, C.L. Philip Chen, Hau San Wong, Jia You

Research output: Journal article publicationJournal articleAcademic researchpeer-review


The class imbalance problem has become a leading challenge. Although conventional imbalance learning methods are proposed to tackle this problem, they have some limitations: 1) undersampling methods suffer from losing important information and 2) cost-sensitive methods are sensitive to outliers and noise. To address these issues, we propose a hybrid optimal ensemble classifier framework that combines density-based undersampling and cost-effective methods through exploring state-of-the-art solutions using multi-objective optimization algorithm. Specifically, we first develop a density-based undersampling method to select informative samples from the original training data with probability-based data transformation, which enables to obtain multiple subsets following a balanced distribution across classes. Second, we exploit the cost-sensitive classification method to address the incompleteness of information problem via modifying weights of misclassified minority samples rather than the majority ones. Finally, we introduce a multi-objective optimization procedure and utilize connections between samples to self-modify the classification result using an ensemble classifier framework. Extensive comparative experiments conducted on real-world data sets demonstrate that our method outperforms the majority of imbalance and ensemble classification approaches.
Original languageEnglish
Pages (from-to)1387-1400
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number4
Publication statusPublished - 2020


  • Cost-sensitive method , ensemble classifier , imbalanced learning , undersampling


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