A hierarchical fusion framework to integrate homogeneous and heterogeneous classifiers for medical decision-making

Linjing Wang, Tianlan Mo, Xuetao Wang, Wentao Chen, Qiang He, Xin Li, Shuxu Zhang, Ruimeng Yang, Jialiang Wu, Xuejun Gu, Jun Wei, Peiliang Xie, Linghong Zhou, Xin Zhen

Research output: Contribution to journalArticlepeer-review

Abstract

Classifier diversity and fusion architecture are two critical characteristics stressed in homogeneous and heterogeneous ensemble learning methods and they are equally important for building a successful multi-classifier system. In this study, we introduced a two-level framework, namely hierarchical fusion of homogeneous and heterogeneous multi-classifiers (HF2HM), to integrate the diversified classification models produced by feeding heterogeneous classifiers with homogeneous random-projected training datasets. The proposed hierarchical fusion scheme was comprehensively validated using fifteen public UCI datasets and three clinical datasets. The experimental results demonstrated the superiority of the proposed HF2HM framework over the base classifiers and the state-of-the-art benchmark ensemble methods, verifying it as a potential tool to assist in medical decision making in practical clinical settings.

Original languageEnglish (US)
Article number106517
JournalKnowledge-Based Systems
Volume212
DOIs
StatePublished - Jan 5 2021

Keywords

  • Ensemble diversity
  • Ensemble method
  • Fusion architecture
  • Heterogeneous ensemble
  • Homogeneous ensemble

ASJC Scopus subject areas

  • Management Information Systems
  • Software
  • Information Systems and Management
  • Artificial Intelligence

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