A survey on machine learning for data fusion

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorMeng, Tongen_US
dc.contributor.authorJing, Xuyangen_US
dc.contributor.authorYan, Zhengen_US
dc.contributor.authorPedrycz, Witolden_US
dc.contributor.departmentDepartment of Communications and Networkingen
dc.contributor.groupauthorNetwork Security and Trusten
dc.contributor.organizationXidian Universityen_US
dc.contributor.organizationUniversity of Albertaen_US
dc.date.accessioned2020-01-17T13:33:18Z
dc.date.available2020-01-17T13:33:18Z
dc.date.embargoinfo:eu-repo/date/embargoEnd/2021-12-24en_US
dc.date.issued2020-05-01en_US
dc.description.abstractData 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.en
dc.description.versionPeer revieweden
dc.format.extent15
dc.format.extent115-129
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationMeng, T, Jing, X, Yan, Z & Pedrycz, W 2020, ' A survey on machine learning for data fusion ', Information Fusion, vol. 57, pp. 115-129 . https://doi.org/10.1016/j.inffus.2019.12.001en
dc.identifier.doi10.1016/j.inffus.2019.12.001en_US
dc.identifier.issn1566-2535
dc.identifier.otherPURE UUID: e25cb857-e0b8-411c-94d7-8a1541045cacen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/e25cb857-e0b8-411c-94d7-8a1541045cacen_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85076856977&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/40174084/ELEC_Meng_Survey_on_Machine_InFFUS.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/42590
dc.identifier.urnURN:NBN:fi:aalto-202001171705
dc.language.isoenen
dc.publisherElsevier
dc.relation.ispartofseriesInformation Fusionen
dc.relation.ispartofseriesVolume 57en
dc.rightsopenAccessen
dc.subject.keywordData fusionen_US
dc.subject.keywordFusion criteriaen_US
dc.subject.keywordFusion methodsen_US
dc.subject.keywordMachine learningen_US
dc.titleA survey on machine learning for data fusionen
dc.typeA2 Katsausartikkeli tieteellisessä aikakauslehdessäfi

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