Journal
INFORMATION FUSION
Volume 57, Issue -, Pages 115-129Publisher
ELSEVIER
DOI: 10.1016/j.inffus.2019.12.001
Keywords
Data fusion; Machine learning; Fusion methods; Fusion criteria
Funding
- NSFC [61672410, 61802293, U1536202]
- Academy of Finland [308087, 314203]
- National Postdoctoral Program for Innovative Talents [BX20180238]
- China Postdoctoral Science Foundation [2018M633461]
- Fundamental Research Funds for the Central Universities [JB191504]
- Shaanxi Innovation Team project [2018TD-007]
- 111 project [B16037]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available