Information fusion for multi-source fuzzy information system with the same structure

Yu, Jianhang; Xu, Weihua

doi:10.1109/icmlc.2015.7340917

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Let the s overlapping pieces of single-source information system form an information box with s levels, as shown Figure 1, which comes from our previous study [35]. …”

Section: Multi-source Information Systemsmentioning

confidence: 99%

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Information Fusion in a Multi-Source Incomplete Information System Based on Information Entropy

Zhang

2017

Entropy

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Abstract:As we move into the information age, the amount of data in various fields has increased dramatically, and data sources have become increasingly widely distributed. The corresponding phenomenon of missing data is increasingly common, and it leads to the generation of incomplete multi-source information systems. In this context, this paper's proposal aims to address the limitations of rough set theory. We study the method of multi-source fusion in incomplete multi-source systems. This paper presents a method for fusing incomplete multi-source systems based on information entropy; in particular, by comparison with another method, our fusion method is validated. Furthermore, extensive experiments are conducted on six UCI data sets to verify the performance of the proposed method. Additionally, the experimental results indicate that multi-source information fusion approaches significantly outperform other approaches to fusion.

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“…Let the s overlapping pieces of single-source information system form an information box with s levels, as shown Figure 1, which comes from our previous study [35]. …”

Section: Multi-source Information Systemsmentioning

confidence: 99%

“…As science has developed, people have found many ways to obtain information. An information box [35] can have multiple information sources, and every information source can be used to construct an information system. If all information sources are incomplete, then they can be used to construct multiple incomplete information systems.…”

Section: Introductionmentioning

confidence: 99%

Information Fusion in a Multi-Source Incomplete Information System Based on Information Entropy

Zhang

2017

Entropy

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Attribute Reduction in Multi-source Decision Systems

Guo

2016

Rough Sets

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Rough set is one of the important methods for rule acquisition and attribute reduction. The current goal of rough set attribute reduction focuses more on minimizing the number of reduced attributes, but ignores the spatial similarity between reduced and decision attributes, which may lead to problems such as increased number of rules and limited generality. In this paper, a rough set attribute reduction algorithm based on spatial optimization is proposed. By introducing the concept of spatial similarity, to find the reduction with the highest spatial similarity, so that the spatial similarity between reduction and decision attributes is higher, and more concise and widespread rules are obtained. In addition, a comparative experiment with the traditional rough set attribute reduction algorithms is designed to prove the effectiveness of the rough set attribute reduction algorithm based on spatial optimization, which has made significant improvements on many datasets. Code Availability: https://github.com/nkjmnkjm/SRS.

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Information Fusion Based on Information Entropy in Fuzzy Multi-source Incomplete Information System

Wang

2016

Int. J. Fuzzy Syst.

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Information fusion for multi-source fuzzy information system with the same structure

Cited by 3 publications

References 10 publications

Information Fusion in a Multi-Source Incomplete Information System Based on Information Entropy

Information Fusion in a Multi-Source Incomplete Information System Based on Information Entropy

Attribute Reduction in Multi-source Decision Systems

Information Fusion Based on Information Entropy in Fuzzy Multi-source Incomplete Information System

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