Information Structures and Uncertainty measures in a Hybrid Information System: Gaussian Kernel Method

Zeng, Jiasheng; Li, Zhaowen; Zhang, Pengfei; Wang, Pei

doi:10.1007/s40815-019-00779-8

Cited by 8 publications

(1 citation statement)

References 47 publications

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“…Some researchers have made some explorations in this respect and achieved a great deal of excellent research results. For instance, Düntsch et al [11] investigated the measurement of decision rules on the basis of Shannon entropy in RST; Li et al [20] gave UM methods based on fuzzy relation in an IS; Zeng et al [42] researched UMs in a hybrid IS with the help of Gaussian kernel; Li et al [17] provided a method to measure the uncertainty of a fully fuzzy IS by means of Gaussian kernel; Yang et al [38] proposed UM methods for multi-source fuzzy IS.…”

Section: Introductionmentioning

confidence: 99%

New Measures of Uncertainty for Interval-Valued Data With Application to Attribute Reduction

2022

IEEE Access

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Uncertainty measurement (UM) gives a brand-new perspective on attribute reduction in an information system (IS). Interval-valued data is a kind of very vital data in rough set theory (RST). Rough set model based on tolerance relations can be considered to deal with interval-valued data. However, these kinds of tolerance relations have deficiencies when they are used in fuzzy rough computation. This paper studies new UM for an interval-valued information system (IVIS) and considers its attribute reduction. Firstly, a novel fuzzy symmetry relation on the object set of an IVIS is established based on "The similarity between information values that is fed back to the attribute set". Secondly, λ-information granules on the basis of a fuzzy symmetry relation are obtained. Then, four UMs for an IVIS are investigated. Next, numerical experiments and statistical tests are used to evaluate the performance of the proposed UMs. Moreover, attribute reduction in an IVIS is studied and the relevant algorithms are proposed. Finally, clustering analysis on the reduced IVIS is conducted. Experimental results indicate that the proposed algorithms are effective based on evaluation indicators of clustering performance. This paper provides a novel viewpoint for the establishment of fuzzy symmetry relation and attribute reduction algorithms.

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Section: Introductionmentioning

confidence: 99%

New Measures of Uncertainty for Interval-Valued Data With Application to Attribute Reduction

2022

IEEE Access

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Data Transmission Reliability Detection of Hybrid Information System Based on Smart Contract

Wang

2022

IoT and Big Data Technologies for Health Care

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A Survey of Homogeneous and Heterogeneous Multi-source Information Fusion Based on Rough Set Theory

Liu,

Tang,

et al. 2024

Communications in Computer and Information Science

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Information Structures and Uncertainty measures in a Hybrid Information System: Gaussian Kernel Method

Cited by 8 publications

References 47 publications

New Measures of Uncertainty for Interval-Valued Data With Application to Attribute Reduction

New Measures of Uncertainty for Interval-Valued Data With Application to Attribute Reduction

Data Transmission Reliability Detection of Hybrid Information System Based on Smart Contract

A Survey of Homogeneous and Heterogeneous Multi-source Information Fusion Based on Rough Set Theory

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