Linguistic Fuzzy Rough Sets for Multi Criteria Group Decision Making

Wang, Hai; Xu, Zeshui

doi:10.2991/seee-15.2015.31

Cited by 2 publications

(3 citation statements)

References 21 publications

(26 reference statements)

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“…These hedges will be utilized to describe the vague properties of fuzzy concepts. Using these modi ers, fuzzy context dependent characteristics of objects could be adequately identi ed, thus, express users' preferences more accurately through adjusting the con dence level of the inferred context [25]. Often, in natural language, humans often use adjectives and adverbs in order to describe what they want.…”

Section: Fuzruf-onto Methodologymentioning

confidence: 99%

“…where inf is the greatest lower bound, and sup is the least upper bound of the set. Various linguistic hedges can be modeled by means of these upper and lower approximations as shown in table1 [3][9] [25] [28]. Approximations such as tight lower, loose, lower, tight upper, and loose upper could be constructed through approximating the lower and the upper fuzzy rough sets.…”

Section: De Nitionmentioning

confidence: 99%

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FUZRUF-onto: a Methodology to Develop Fuzzy Rough Ontologies

sanyour

2024

Preprint

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Nowadays, semantic web technologies play a crucial role in knowledge representation paradigm. With the raise of imprecise and vague knowledge, there is an upsurge demand in applying a concrete well-established procedure to represent such knowledge. Ontologies, particularly fuzzy ontologies are increasingly applied in application scenarios in which handling of vague knowledge is significant. However, such fuzzy ontologies utilize fuzzy set theory to provide quantitative methods to manage vagueness. In various cases of real-life scenarios, people need to express their everyday requirements using linguistic adverbs such as very, exactly, mostly, possibly, etc. The aim is to show how fuzzy properties can be complemented by Rough Set methods to capture another type of imprecision caused by approximation spaces. Rough sets theory offers a qualitative approach to model such vagueness via describing fuzzy properties at multiple levels of granularity using approximation sets. Using rough-set theory, each fuzzy concept is represented by two approximations. The lower approximation PL(C) consists of a set of fuzzy properties that are definitely observable in the concept. The upper approximation PU(C) on the other hand contains fuzzy properties that are possibly associated with the concept but may not be observed. This paper introduces a methodology named FUZRUF-onto methodology, which is a formal guidance on how to build fuzzy rough ontologies from scratch using extensive research in the area of fuzzy rough combination. Fuzzy set and rough set theories are applied to capture the inherently fuzzy relationships among concepts expressed by natural languages. The methodology provides a very good guideline for formally constructing fuzzy rough ontologies in terms of completeness, correctness, consistency, understandability, and conciseness. To explain how the FUZRUF-onto works, and demonstrate its usefulness, a practical step by step example is provided.

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Section: Fuzruf-onto Methodologymentioning

confidence: 99%

Section: De Nitionmentioning

confidence: 99%

FUZRUF-onto: a Methodology to Develop Fuzzy Rough Ontologies

sanyour

2024

Preprint

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“…Cuong [15] introduced picture fuzzy sets. Wang et al [16] established PFVIKOR method for effective construction of project management team. Mete et al [17] applied PF VIKOR for risk estimation in construction of natural gas pipeline.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Efficiency of Building Stones in Complex Fuzzy Environment

Shanthi

Umamakeswari

2023

E3S Web of Conf.

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Rocks are importantly used as building materials. Here chemical, biochemical, clastic and organic sedimentary rocks are chosen for building stones. These rocks are tested for their efficiency in structure strength, toughness, weathering resistances and quality. Using complex picture fuzzy soft VIKOR method it is concluded that clastic sedimentary rocks is the best building stone. First a score function is defined and is used for the construction of complex picture fuzzy soft score matrix the proficiency of CPFS score function over the existing score function is established. Next entropy measure is determined from which weight values are calculated. Complex picture fuzzy soft positive and negative ideal solutions are found and hence the group utility and individual regret values are obtained. By making use of these values the compromising ranking solution is found.

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Linguistic Fuzzy Rough Sets for Multi Criteria Group Decision Making

Cited by 2 publications

References 21 publications

FUZRUF-onto: a Methodology to Develop Fuzzy Rough Ontologies

FUZRUF-onto: a Methodology to Develop Fuzzy Rough Ontologies

Estimating the Efficiency of Building Stones in Complex Fuzzy Environment

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