Feasibility study of a multi-criteria decision-making based hierarchical model for multi-modality feature and multi-classifier fusion: Applications in medical prognosis prediction

Qiang He, Xin Li, D. W.Nathan Kim, Xun Jia, Xuejun Gu, Xin Zhen, Linghong Zhou

Research output: Contribution to journalArticle


Radiomics has great prospects in terms of tumour grading, diagnosis and prediction of prognosis by analysing multifaceted data from sources such as clinical treatments, medical images, and pathology. However, exploring an effective way to manage miscellaneous clinical information, as well as to select an appropriate classifier for prediction modelling, is still demanding in a practical clinical context. In this study, we propose a multi-criterion decision-making (MCDM) based classifier fusion (MCF) strategy to combine different classifiers within an MCDM framework. A hierarchical predictive scheme (H-MCF) based on the proposed MCF is also investigated to reliably link the multi-modality features and multi-classifiers. Ten public UCI datasets and two clinical datasets were used to validate the proposed MCF and H-MCF. The experimental results showed that H-MCF has superior predictive performance when compared with the traditional fusion strategies and other fusion architectures, thus demonstrating the feasibility of the proposed H-MCF in integrating information from features of diversified modalities and different classifiers.

Original languageEnglish (US)
Pages (from-to)207-219
Number of pages13
JournalInformation Fusion
StatePublished - Mar 1 2020



  • Classifier fusion
  • Multi-classifier
  • Multi-modality
  • Multiple criteria decision making
  • Radiomics

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems
  • Hardware and Architecture

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