Progressive Hybrid Classifier Ensemble for Imbalanced Data

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
View graph of relations

Author(s)

  • Kaixiang Yang
  • Zhiwen Yu
  • C. L. Philip Chen
  • Jane You
  • Guoqiang Han

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)2464-2478
Journal / PublicationIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume52
Issue number4
Online published2 Feb 2021
Publication statusPublished - Apr 2022

Abstract

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.

Research Area(s)

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

Citation Format(s)

Progressive Hybrid Classifier Ensemble for Imbalanced Data. / Yang, Kaixiang; Yu, Zhiwen; Chen, C. L. Philip; Cao, Wenming; Wong, Hau-San; You, Jane; Han, Guoqiang.

In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 52, No. 4, 04.2022, p. 2464-2478.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review