Fuzzy rough set models over two universes using textures

Diker, Murat; Uğur, Ayşegül Altay

doi:10.1016/j.fss.2021.10.014

Cited by 6 publications

(1 citation statement)

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“…In fuzzy -covering group approximation spaces, the defined fuzzy rough set models are explained by Zhang [34]. The noiseaware (FRS) approach to choose attribute selection is discussed by Yang et al [35]. The Using textures, the fuzzy rough set models span two universes is developed by Diker [36].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach Toward Q-Rung Orthopair Fuzzy Rough Dombi Aggregation Operators and Their Application to Decision-Making Problems

Khan

Khan³

et al. 2023

IEEE Access

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It is the purpose of this paper to present and explain the fuzzy set of concepts Zadeh that are discussed in membership grade. We also demonstrate that three-way analysis is crucial in decision-making systems. In order to implement a three-way decision, various elements are required, such as conditional probability, estimated loss, and decision rules. The objective is to describe first the Q-ROFRS (Q-Rung Orthopair Fuzzy Rough Set) to define some fundamental operational rules under the dombi norms. The use of these operation laws can allow us to define some aggregation operators, which include Q-ROFWA (Q-Rung Orthopair Fuzzy Rough Weighted Average), Q-ROFROWA (Q-Rung Orthopair Fuzzy Rough Order Weighted Average), and Q-ROFHWA, which are all based on Q-Rung Orthopair Fuzzy Rough Hybrid Weighted Averages. By applying these operation laws, we can use these operator laws to calculate expert information that can be utilized in the calculation process. Ultimately, we have applied all three-way process rules to rank and find the best results possible. To determine the accuracy and relevance of each piece of work, we conducted a comparative analysis with other recent works that have been published

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Section: Introductionmentioning

confidence: 99%