Progressive Hybrid Classifier Ensemble for Imbalanced Data

Kaixiang Yang, Zhiwen Yu, C. L. Philip Chen, Hau-San Wong, Wenming Cao, Jia You, Guoqiang Han

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)


The class imbalance problem has posed a leading challenge in real-world applications. Traditional methods focus on either the data level or algorithm level to solve the binary classification problem on imbalanced data, and seldom consider searching an effective transformation for classification. Besides, the undersampling process adopted in them is always subjective and unilateral. To address the above issues, we first propose a hybrid classifier ensemble (HCE) framework to conduct binary imbalanced data classification, which mainly includes a metric-based data space transformation (MDST) and an adaptive two-stage undersampling process (ATUP). The MDST aims to find a more appropriate embedding space for original imbalance data sets, and the ATUP considers both informative and representative samples to generate balanced data sets. Furthermore, we design a progressive HCE (PHCE) framework to improve the performance of HCE by utilizing a progressive mechanism with local and global evaluation criteria to select ensemble members. Extensive comparative experiments conducted on 28 real-world data sets exhibit that our method PHCE outperforms the majority of imbalance ensemble classification approaches.
Original languageEnglish
Article number21643629
Pages (from-to)2464 - 2478
Number of pages15
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number4
Publication statusPublished - 1 Apr 2022


  • Adaptive undersampling
  • binary classification
  • imbalanced learning
  • metric learning
  • progressive ensemble

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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