On the belief structures and reductions of multigranulation spaces with decisions

Tan, Anhui; Wu, Wei-Zhi; Tao, Yuzhi

doi:10.1016/j.ijar.2017.05.005

Cited by 24 publications

(3 citation statements)

References 46 publications

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“…Future studies can extend the belief intervals combination rule in the following directions. There are numerous aggregation operators in the fuzzy sets domain, in this article, we only select the IFWA operator to construct the belief intervals combination rule and some other aggregation operators are also expected to be used in the combination rules of DST. Moreover, the DST has been generated with such focal sets as multigranulation spaces, discrete belief, fuzzy sets, multidimensional data, belief with missing data values, and belief with various types of belief . It will be interesting to extend the belief intervals combination rule these generalized DST.…”

Section: Discussionmentioning

confidence: 99%

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Belief intervals aggregation

Sun¹,

Guan²,

Xiao³

et al. 2018

Int. J. Intell. Syst.

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The combination rule is the core of Dempster-Shafer theory (DST), and there is no uniform aggregation rule adapting to all conditions. The construction of such a ruleis still an open and hot topic. In this article, we focus on this point. We focus on the belief interval, which is made up of belief function and plausibility function, in the form of [belief function, plausibility function], instead of basic belief assignment, to represent the DST. We aim at exploring a belief interval combination rule as the combination rule of DST. To do this, we contrast the belief interval with the intuitionistic fuzzy sets and construct the belief interval combination rule based on the intuitionistic fuzzy weighted averaging (IFWA) operator, which opens the door for DST combination rules to the aggregation operator perspective. Further, we find that directly using the IFWA operator as the belief interval combination rule poses three problems: the "'one' veto problem," converge easily close to [1,1] and the belief function from the belief interval combination rule is not normalized. To solve these problems, we add a normalization process in the belief interval combination rule to modify it, which can address all these three problems well.We also set up a series of examples to illustrate the belief interval combination rule, including a multisensor fusion scenario to compare it with some of the existing rules, which shows its superiorities in better stability and lower operational load. K E Y W O R D S aggregation operator, belief functions, belief intervals, combination rule, plausibility function Int J Intell Syst. 2018;2425-2447.wileyonlinelibrary.com/journal/int

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Section: Discussionmentioning

confidence: 99%

“…Moreover, the DST has been generated with such focal sets as multigranulation spaces, discrete belief, fuzzy sets, multidimensional data, belief with missing data values, and belief with various types of belief . It will be interesting to extend the belief intervals combination rule these generalized DST.…”

Section: Discussionmentioning

confidence: 99%

Belief intervals aggregation

Sun¹,

Guan²,

Xiao³

et al. 2018

Int. J. Intell. Syst.

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“…In order to overcome the problem of updating reduction, when the large-scale data vary dynamically, Jing et al developed an incremental attribute reduction approach based on knowledge granularity with a multi-granulation view [13]. Then other multi-granulation reduction methods have been put forward one after another [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Novel Three-Way Decisions Models with Multi-Granulation Rough Intuitionistic Fuzzy Sets

Xue

Han

et al. 2018

Symmetry

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The existing construction methods of granularity importance degree only consider the direct influence of single granularity on decision-making; however, they ignore the joint impact from other granularities when carrying out granularity selection. In this regard, we have the following improvements. First of all, we define a more reasonable granularity importance degree calculating method among multiple granularities to deal with the above problem and give a granularity reduction algorithm based on this method. Besides, this paper combines the reduction sets of optimistic and pessimistic multi-granulation rough sets with intuitionistic fuzzy sets, respectively, and their related properties are shown synchronously. Based on this, to further reduce the redundant objects in each granularity of reduction sets, four novel kinds of three-way decisions models with multi-granulation rough intuitionistic fuzzy sets are developed. Moreover, a series of concrete examples can demonstrate that these joint models not only can remove the redundant objects inside each granularity of the reduction sets, but also can generate much suitable granularity selection results using the designed comprehensive score function and comprehensive accuracy function of granularities.

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Knowledge reduction of pessimistic multigranulation rough sets in incomplete information systems

Zhang

Lin

2021

Soft Comput

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On the belief structures and reductions of multigranulation spaces with decisions

Cited by 24 publications

References 46 publications

Belief intervals aggregation

Belief intervals aggregation

Novel Three-Way Decisions Models with Multi-Granulation Rough Intuitionistic Fuzzy Sets

Knowledge reduction of pessimistic multigranulation rough sets in incomplete information systems

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