On measurements of covering rough sets based on granules and evidence theory

Chen, Degang; Zhang, Xiaoxia; Li, Wanlu

doi:10.1016/j.ins.2015.04.051

Cited by 34 publications

(13 citation statements)

References 61 publications

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“…Table 5 and Fig. 4 both show the comparison of time complexity among different rough set measures, like covering rough set [25], traditional…”

Section: (Iii) According To the Conjunction Of Theoremmentioning

confidence: 99%

“…As introduced in Yao et al [24], the adequate condition for belief structure exactly exists in the classic rough set. On the above basis, this study was extended to covering rough set by Chen et al [25,26], who successfully employ the belief function and the plausibility function to describe the upper and lower approximations of the covering rough set, which means the numerical features of the rough set can be characterized by evidence theory. In particular, from the perspective of information fusion, Lin et al [27] explore the relationship between evidence theory and classical multi-granulation rough sets, which shows that, in general, the classic optimistic multi-granulation rough set does not have its corresponding belief structure.…”

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confidence: 99%

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A data fusion method in wireless sensor network based on belief structure

Long

Liu

Yang

et al. 2021

J Wireless Com Network

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Considering the issue with respect to the high data redundancy and high cost of information collection in wireless sensor nodes, this paper proposes a data fusion method based on belief structure to reduce attribution in multi-granulation rough set. By introducing belief structure, attribute reduction is carried out for multi-granulation rough sets. From the view of granular computing, this paper studies the evidential characteristics of incomplete multi-granulation ordered information systems. On this basis, the positive region reduction, belief reduction and plausibility reduction are put forward in incomplete multi-granulation ordered information system and analyze the consistency in the same level and transitivity in different levels. The positive region reduction and belief reduction are equivalent, and the positive region reduction and belief reduction are unnecessary and sufficient conditional plausibility reduction in the same level, if the cover structure order of different levels are the same the corresponding equivalent positive region reduction. The algorithm proposed in this paper not only performs three reductions, but also reduces the time complexity largely. The above study fuses the node data which reduces the amount of data that needs to be transmitted and effectively improves the information processing efficiency.

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“…Table 5 and Fig. 4 both show the comparison of time complexity among different rough set measures, like covering rough set [25], traditional…”

Section: (Iii) According To the Conjunction Of Theoremmentioning

confidence: 99%

mentioning

confidence: 99%

A data fusion method in wireless sensor network based on belief structure

Long

Liu

Yang

et al. 2021

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…In rough set theory, the precision and rough degrees of a rough set are utilized to describe the uncertainty measures of the set with respect to an approximation space [7] . However, the precision and rough degrees cannot be directly extended to characterize the uncertainty of an IF rough set.…”

Section: Article In Pressmentioning

confidence: 99%

An intuitionistic fuzzy graded covering rough set

Huang

Guo

et al. 2016

Knowledge-Based Systems

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“…ere exist strong connections between the Dempster-Shafer theory of evidence and the rough set theory. For example, the relationships between the belief functions and covering rough sets are discussed [23,[43][44][45]. Furthermore, the evidence theory was used to characterize knowledge reductions for covering rough sets in covering information systems [23,[46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Numerical Characterizations of Topological Reductions of Covering Information Systems in Evidence Theory

Zhang

2021

Mathematical Problems in Engineering

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The reductions of covering information systems in terms of covering approximation operators are one of the most important applications of covering rough set theory. Based on the connections between the theory of topology and the covering rough set theory, two kinds of topological reductions of covering information systems are discussed in this paper, which are characterized by the belief and plausibility functions from the evidence theory. The topological spaces by two pairs of covering approximation operators in covering information systems are pseudo-discrete, which deduce partitions. Then, using plausibility function values of the sets in the partitions, the definitions of significance and relative significance of coverings are presented. Hence, topological reduction algorithms based on the evidence theory are proposed in covering information systems, and an example is adopted to illustrate the validity of the algorithms.

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On measurements of covering rough sets based on granules and evidence theory

Cited by 34 publications

References 61 publications

A data fusion method in wireless sensor network based on belief structure

A data fusion method in wireless sensor network based on belief structure

An intuitionistic fuzzy graded covering rough set

Numerical Characterizations of Topological Reductions of Covering Information Systems in Evidence Theory

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