Multi-source information fusion based on rough set theory: A review

Multi-Source Information Fusion (MSIF) is a comprehensive and interdisciplinary subject, and is referred to as, multi-sensor information fusion which was originated in the 1970s. Nowadays, the types and updates of data are becoming more multifarious and frequent, which bring new challenges for infor...

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Published inInformation fusion Vol. 68; pp. 85 - 117
Main Authors Zhang, Pengfei, Li, Tianrui, Wang, Guoqiang, Luo, Chuan, Chen, Hongmei, Zhang, Junbo, Wang, Dexian, Yu, Zeng
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.04.2021
Subjects
Cluster ensembles
Homogeneous and heterogeneous MSIF
Incremental learning
Multi-source information fusion
Multi-view rough sets
Parallel computing
Rough set theory
Rough set theory
Parallel computing
Incremental learning
Homogeneous and heterogeneous MSIF
Multi-source information fusion
Multi-view rough sets
Cluster ensembles
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Abstract Multi-Source Information Fusion (MSIF) is a comprehensive and interdisciplinary subject, and is referred to as, multi-sensor information fusion which was originated in the 1970s. Nowadays, the types and updates of data are becoming more multifarious and frequent, which bring new challenges for information fusion to deal with the multi-source data. Consequently, the construction of MSIF models suitable for different scenarios and the application of different fusion technologies are the core problems that need to be solved urgently. Rough set theory (RST) provides a computing paradigm for uncertain data modeling and reasoning, especially for classification issues with noisy, inaccurate or incomplete data. Furthermore, due to the rapid development of MSIF in recent years, the methodologies of learning under RST are becoming increasingly mature and systematic, unveiling a framework which has not been mentioned in the literature. In order to better clarify the approaches and application of MSIF in RST research community, this paper reviews the existing models and technologies from the perspectives of MSIF model (i.e., homogeneous and heterogeneous MSIF model), multi-view rough sets information fusion model (i.e., multi-granulation, multi-scale and multi-view decisions information fusion models), parallel computing information fusion model, incremental learning fusion technology and cluster ensembles fusion technology. Finally, RST based MSIF related research directions and challenges are also covered and discussed. By providing state-of-the-art understanding in specialized literature, this survey will directly help researchers understand the research developments of MSIF under RST. •Homogeneous and heterogeneous MSIF models are systematically introduced.•Multi-view rough set is discussed by combining several rough set models.•Parallel computing model based rough sets is reviewed in view of information fusion.•Incremental learning fusion technology based rough sets is surveyed.•Cluster ensembles and three-way clustering fusion approaches based rough sets are overviewed.
AbstractList Multi-Source Information Fusion (MSIF) is a comprehensive and interdisciplinary subject, and is referred to as, multi-sensor information fusion which was originated in the 1970s. Nowadays, the types and updates of data are becoming more multifarious and frequent, which bring new challenges for information fusion to deal with the multi-source data. Consequently, the construction of MSIF models suitable for different scenarios and the application of different fusion technologies are the core problems that need to be solved urgently. Rough set theory (RST) provides a computing paradigm for uncertain data modeling and reasoning, especially for classification issues with noisy, inaccurate or incomplete data. Furthermore, due to the rapid development of MSIF in recent years, the methodologies of learning under RST are becoming increasingly mature and systematic, unveiling a framework which has not been mentioned in the literature. In order to better clarify the approaches and application of MSIF in RST research community, this paper reviews the existing models and technologies from the perspectives of MSIF model (i.e., homogeneous and heterogeneous MSIF model), multi-view rough sets information fusion model (i.e., multi-granulation, multi-scale and multi-view decisions information fusion models), parallel computing information fusion model, incremental learning fusion technology and cluster ensembles fusion technology. Finally, RST based MSIF related research directions and challenges are also covered and discussed. By providing state-of-the-art understanding in specialized literature, this survey will directly help researchers understand the research developments of MSIF under RST. •Homogeneous and heterogeneous MSIF models are systematically introduced.•Multi-view rough set is discussed by combining several rough set models.•Parallel computing model based rough sets is reviewed in view of information fusion.•Incremental learning fusion technology based rough sets is surveyed.•Cluster ensembles and three-way clustering fusion approaches based rough sets are overviewed.
Author Yu, Zeng
Li, Tianrui
Wang, Guoqiang
Luo, Chuan
Zhang, Junbo
Zhang, Pengfei
Wang, Dexian
Chen, Hongmei
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Snippet Multi-Source Information Fusion (MSIF) is a comprehensive and interdisciplinary subject, and is referred to as, multi-sensor information fusion which was...
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SubjectTerms Cluster ensembles
Homogeneous and heterogeneous MSIF
Incremental learning
Multi-source information fusion
Multi-view rough sets
Parallel computing
Rough set theory
Title Multi-source information fusion based on rough set theory: A review
URI https://dx.doi.org/10.1016/j.inffus.2020.11.004
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