A survey on machine learning for data fusion

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A survey on machine learning for data fusion. / Meng, Tong; Jing, Xuyang; Yan, Zheng; Pedrycz, Witold.

julkaisussa: Information Fusion, Vuosikerta 57, 01.05.2020, s. 115-129.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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Meng, Tong ; Jing, Xuyang ; Yan, Zheng ; Pedrycz, Witold. / A survey on machine learning for data fusion. Julkaisussa: Information Fusion. 2020 ; Vuosikerta 57. Sivut 115-129.

Bibtex - Lataa

@article{e25cb857e0b8411c94d78a1541045cac,
title = "A survey on machine learning for data fusion",
abstract = "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.",
keywords = "Data fusion, Fusion criteria, Fusion methods, Machine learning",
author = "Tong Meng and Xuyang Jing and Zheng Yan and Witold Pedrycz",
year = "2020",
month = "5",
day = "1",
doi = "10.1016/j.inffus.2019.12.001",
language = "English",
volume = "57",
pages = "115--129",
journal = "Information Fusion",
issn = "1566-2535",
publisher = "Elsevier",

}

RIS - Lataa

TY - JOUR

T1 - A survey on machine learning for data fusion

AU - Meng, Tong

AU - Jing, Xuyang

AU - Yan, Zheng

AU - Pedrycz, Witold

PY - 2020/5/1

Y1 - 2020/5/1

N2 - 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.

AB - 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.

KW - Data fusion

KW - Fusion criteria

KW - Fusion methods

KW - Machine learning

UR - http://www.scopus.com/inward/record.url?scp=85076856977&partnerID=8YFLogxK

U2 - 10.1016/j.inffus.2019.12.001

DO - 10.1016/j.inffus.2019.12.001

M3 - Review Article

VL - 57

SP - 115

EP - 129

JO - Information Fusion

JF - Information Fusion

SN - 1566-2535

ER -

ID: 40174079