Uncertainty measurement for incomplete interval-valued information systems based on α-weak similarity

Dai, Jianhua; Wei, Bingjie; Zhang, Xiaohong; Zhang, Qilai

doi:10.1016/j.knosys.2017.09.009

Cited by 77 publications

(13 citation statements)

References 48 publications

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“…2) Dai et al [13] considered UM for an incomplete interval-valued information system (IIVIS) based on α-weak similarity. Firstly, they defined the maximum and minimum similarity degrees in an an IIVIS, and α-weak similarity relation.…”

Section: Comparison and Discussionmentioning

confidence: 99%

Uncertainty Measurement for Fuzzy Set-Valued Data

Wang

Tang

2020

IEEE Access

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Uncertainty measurement (UM) can offer new visual angle for data analysis. A fuzzy set-valued information system (FSVIS) indicates an information system (IS) where its information values are fuzzy sets. This article investigates UM for fuzzy set-valued data based on Chebyshev distance. First, the distance between information values is founded in a given subsystem. After that, the tolerance relation induced by this subsystem is obtained by means of this distance. Moreover, the information structure of this subsystem is proposed. Next, the uncertainty of a FSVIS are measured. Eventually, to show the feasibility of the proposed measures, effectiveness analysis is carried out from a statistical view. The obtained outcomes may be helpful for comprehending the nature of uncertainty in a FSVIS.

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Section: Comparison and Discussionmentioning

confidence: 99%

Uncertainty Measurement for Fuzzy Set-Valued Data

Wang

Tang

2020

IEEE Access

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“…For example, Yao et al [9] presented a granularity measure on the viewpoint of granulation; Wierman [29] provided measures of uncertainty and granularity in RST; Bianucci et al [41,42] explored entropy and co-entropy approaches for uncertainty measurements of coverings; Yao [25] studied several types of information-theoretical measures for attribute importance in RST; Beaubouef et al [43] proposed a method for measuring the uncertainty of rough sets. Liang et al [44,45] investigated information granulation in complete information systems; Dai et al [46] researched entropy and granularity measures for SISs; Qian et al [47,48] presented the axiomatic definition of information granulation in a knowledge base and examined information granularity of a fuzzy relation by using its fuzzy granular structure; Xu et al [49] considered knowledge granulation in ordered information systems; Dai et al [50] studied the uncertainty of incomplete interval-valued information systems based on α-weak similarity; Xie et al [51] put forward new uncertainty measurement for an interval-valued information system; Zhang et al [52] measured the uncertainty of a fully fuzzy information system.…”

Section: Research Background and Related Workmentioning

confidence: 99%

Uncertainty Measurement for a Set-Valued Information System: Gaussian Kernel Method

Wang

2019

Symmetry

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A set-valued information system (SIS) is the generalization of a single-valued information system. This article explores uncertainty measurement for a SIS by using Gaussian kernel. The fuzzy T cos -equivalence relation lead by a SIS is first obtained by using Gaussian kernel. Then, information structures in this SIS are described by set vectors. Next, dependence between information structures is presented and properties of information structures are investigated. Lastly, uncertainty measures of a SIS are presented by using its information structures. Moreover, effectiveness analysis is done to assess the feasibility of our presented measures. The consequence of this article will help us understand the intrinsic properties of uncertainty in a SIS.

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“…The abovementioned RS models are all under the complete information system (IS), but in reality, the IS encountered are often incomplete, such as data integration [38], data mining [39], fault diagnosis [40], uncertainty measurement [41], and many others [42][43][44]. Therefore, a large number of studies on Incomplete Information System (IIS) have emerged [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61].…”

Section: Introductionmentioning

confidence: 99%

Double-Quantitative Generalized Multi-Granulation Set-Pair Dominance Rough Sets in Incomplete Ordered Information System

Xue

Zhang

et al. 2020

Symmetry

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Since the rough sets theory based on the double quantification method was proposed, it has attracted wide attention in decision-making. This paper studies the decision-making approach in Incomplete Ordered Information System (IOIS). Firstly, to better extract the effective information in IOIS, combined with the advantages of set-pair dominance relation and generalized multi-granulation, the generalized multi-granulation set-pair dominance variable precision rough sets (GM-SPD-VPRS) and the generalized multi-granulation set-pair dominance graded rough sets (GM-SPD-GRS) are proposed. Moreover, we discuss their related properties. Secondly, considering the GM-SPD-VPRS and the GM-SPD-GRS describe information from relative view and absolute view, respectively, we further combine the two rough sets to obtain six double-quantitative generalized multi-granulation set-pair dominance rough sets (GM-SPD-RS) models. Among them, the first two models fuse the approximation operators of two rough sets, and investigate the extreme cases of optimistic and pessimistic. The last four models combine the two rough sets by the logical disjunction operator and the logical conjunction operator. Then, we discuss relevant properties and derive the corresponding decision rules. According to the decision rules, an associated algorithm is constructed for one of the models to calculate the rough regions. Finally, we validate the effectiveness of these models with a medical example. The results indicate that the model is effective for dealing with practical problems.

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Uncertainty measurement for incomplete interval-valued information systems based on α-weak similarity

Cited by 77 publications

References 48 publications

Uncertainty Measurement for Fuzzy Set-Valued Data

Uncertainty Measurement for Fuzzy Set-Valued Data

Uncertainty Measurement for a Set-Valued Information System: Gaussian Kernel Method

Double-Quantitative Generalized Multi-Granulation Set-Pair Dominance Rough Sets in Incomplete Ordered Information System

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