A survey on machine learning for data fusion

Tong Meng, Xuyang Jing, Zheng Yan*, Witold Pedrycz

*Corresponding author for this work

Research output: Contribution to journalReview Articlepeer-review

345 Citations (Scopus)
1213 Downloads (Pure)


Data fusion is a prevalent way to deal with imperfect raw data for capturing reliable, valuable and accurate information. Comparing with a range of classical probabilistic data fusion techniques, machine learning method that automatically learns from past experiences without explicitly programming, remarkably renovates fusion techniques by offering the strong ability of computing and predicting. Nevertheless, the literature still lacks a thorough review of the recent advances of machine learning for data fusion. Therefore, it is beneficial to review and summarize the state of the art in order to gain a deep insight on how machine learning can benefit and optimize data fusion. In this paper, we provide a comprehensive survey on data fusion methods based on machine learning. We first offer a detailed introduction to the background of data fusion and machine learning in terms of definitions, applications, architectures, processes, and typical techniques. Then, we propose a number of requirements and employ them as criteria to review and evaluate the performance of existing fusion methods based on machine learning. Through the literature review, analysis and comparison, we finally come up with a number of open issues and propose future research directions in this field.

Original languageEnglish
Pages (from-to)115-129
Number of pages15
JournalInformation Fusion
Publication statusPublished - 1 May 2020
MoE publication typeA2 Review article, Literature review, Systematic review


  • Data fusion
  • Fusion criteria
  • Fusion methods
  • Machine learning


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