Fuzzy Systems and Decision Making Theory

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Logic".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3865

Special Issue Editors


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Guest Editor
Department of Engineering Management, School of Civil Engineering, Wuhan University, Wuhan 430072, China
Interests: engineering management; decision support; computational semantics analysis; group decision making; computing with words
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2E1, Canada
Interests: fuzzy set theory; pattern clustering; learning (artificial intelligence); decision making; granular
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fuzzy sets theory has gained significant attention from academia and practitioners and has been significantly pursued in the realm of various applications. This theory also delivers a conceptual and algorithmic framework to facilitate the characterization and modeling of human-centric systems. Fuzzy sets have been shown to be an effective, convenient and powerful tool in human and decision-centricity-featured computational Intelligence systems. As such, fuzzy sets contribute to numerous areas, in particular data mining and analytics, decision aid and support, decision analysis, preference modeling, image interpretation, pattern recognition, recommender systems, and explainable artificial intelligence (XAI), to name just a few. 

Among numerous applications, the methodology of fuzzy modeling and systems is particularly useful and convenient in decision model building, decision method analyzing, and decision procedure implementation, wherein decision makers are allowed more or less involvement and intervention. Moreover, as one of the ways of representing uncertain data, fuzzy information and its several extensions are with wide applicability in multi-criteria decision-making and evaluation problems with a multitude of different backgrounds. Besides, some other new data types often enable the mutual study, complementation, comparison and analysis between them and fuzzy granule; for example, the recently introduced basic uncertain information (BUI) well complements the fuzzy information in practical applications. Fuzzy systems and fuzzy sets extensions have also been applied in a large number of group decision-making and rules-based decision-making environments, as well as in a variety of disciplines of science, engineering, economy and management. 

This Special Issue will gather research works and reviews that advance the state-of-the-art concepts, methodologies, algorithms and applications in several emerging and related topics. Special attention to this Special Issue will be paid to the following research topics: fuzzy rules-based and data-driven fuzzy modeling, fuzzy information fusion, fuzzy logic, preference and uncertainty, etc. in multi-criteria decision-making, large-scale decision-making and group decision-making.

Dr. Lesheng Jin
Dr. Zhen-Song Chen
Prof. Dr. Witold Pedrycz
Prof. Dr. Luis Martínez López
Guest Editors

Manuscript Submission Information

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Keywords

  • fuzzy information fusion
  • fuzzy integrals and fuzzy operators
  • fuzzy rules-based decision making
  • group decision making
  • large scale decision making (LSDM)
  • linguistic aggregation fusion and decision making
  • multi-criteria decision making
  • preference involved decision making
  • uncertain involved decision making

Published Papers (3 papers)

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Research

18 pages, 801 KiB  
Article
An Improved Intuitionistic Fuzzy Decision-Theoretic Rough Set Model and Its Application
by Wajid Ali, Tanzeela Shaheen, Hamza Ghazanfar Toor, Tmader Alballa, Alhanouf Alburaikan and Hamiden Abd El-Wahed Khalifa
Axioms 2023, 12(11), 1003; https://doi.org/10.3390/axioms12111003 - 24 Oct 2023
Viewed by 825
Abstract
The Decision-Theoretic Rough Set model stands as a compelling advancement in the realm of rough sets, offering a broader scope of applicability. This approach, deeply rooted in Bayesian theory, contributes significantly to delineating regions of minimal risk. Within the Decision-Theoretic Rough Set paradigm, [...] Read more.
The Decision-Theoretic Rough Set model stands as a compelling advancement in the realm of rough sets, offering a broader scope of applicability. This approach, deeply rooted in Bayesian theory, contributes significantly to delineating regions of minimal risk. Within the Decision-Theoretic Rough Set paradigm, the universal set undergoes a tripartite division, where distinct regions emerge and losses are intelligently distributed through the utilization of membership functions. This research endeavors to present an enhanced and more encompassing iteration of the Decision-Theoretic Rough Set framework. Our work culminates in the creation of the Generalized Intuitionistic Decision-Theoretic Rough Set (GI-DTRS), a fusion that melds the principles of Decision-Theoretic Rough Sets and intuitionistic fuzzy sets. Notably, this synthesis bridges the gaps that exist within the conventional approach. The innovation lies in the incorporation of an error function tailored to the hesitancy grade inherent in intuitionistic fuzzy sets. This integration harmonizes seamlessly with the contours of the membership function. Furthermore, our methodology deviates from established norms by constructing similarity classes based on similarity measures, as opposed to relying on equivalence classes. This shift holds particular relevance in the context of aggregating information systems, effectively circumventing the challenges associated with the process. To demonstrate the practical efficacy of our proposed approach, we delve into a concrete experiment within the information technology domain. Through this empirical exploration, the real-world utility of our approach becomes vividly apparent. Additionally, a comprehensive comparative analysis is undertaken, juxtaposing our approach against existing techniques for aggregation and decision modeling. The culmination of our efforts is a well-rounded article, punctuated by the insights, recommendations, and future directions delineated by the authors. Full article
(This article belongs to the Special Issue Fuzzy Systems and Decision Making Theory)
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18 pages, 330 KiB  
Article
Some Construction Methods for Pseudo-Overlaps and Pseudo-Groupings and Their Application in Group Decision Making
by Diego García-Zamora, Rui Paiva, Anderson Cruz, Javier Fernandez and Humberto Bustince
Axioms 2023, 12(6), 589; https://doi.org/10.3390/axioms12060589 - 14 Jun 2023
Viewed by 667
Abstract
In many real-world scenarios, the importance of different factors may vary, making commutativity an unreasonable assumption for aggregation functions, such as overlaps or groupings. To address this issue, researchers have introduced pseudo-overlaps and pseudo-groupings as their corresponding non-commutative generalizations. In this paper, we [...] Read more.
In many real-world scenarios, the importance of different factors may vary, making commutativity an unreasonable assumption for aggregation functions, such as overlaps or groupings. To address this issue, researchers have introduced pseudo-overlaps and pseudo-groupings as their corresponding non-commutative generalizations. In this paper, we explore various construction methods for obtaining pseudo-overlaps and pseudo-groupings using overlaps, groupings, fuzzy negations, convex sums, and Riemannian integration. We then show the applicability of these construction methods in a multi-criteria group decision-making problem, where the importance of both the considered criteria and the experts vary. Our results highlight the usefulness of pseudo-overlaps and pseudo-groupings as a non-commutative alternative to overlaps and groupings. Full article
(This article belongs to the Special Issue Fuzzy Systems and Decision Making Theory)
16 pages, 1444 KiB  
Article
An Improved Algorithm for Identification of Dominating Vertex Set in Intuitionistic Fuzzy Graphs
by Nazia Nazir, Tanzeela Shaheen, LeSheng Jin and Tapan Senapati
Axioms 2023, 12(3), 289; https://doi.org/10.3390/axioms12030289 - 09 Mar 2023
Cited by 2 | Viewed by 1017
Abstract
In graph theory, a “dominating vertex set” is a subset of vertices in a graph such that every vertex in the graph is either a member of the subset or adjacent to a member of the subset. In other words, the vertices in [...] Read more.
In graph theory, a “dominating vertex set” is a subset of vertices in a graph such that every vertex in the graph is either a member of the subset or adjacent to a member of the subset. In other words, the vertices in the dominating set “dominate” the remaining vertices in the graph. Dominating vertex sets are important in graph theory because they can help us understand and analyze the behavior of a graph. For example, in network analysis, a set of dominant vertices may represent key nodes in a network that can influence the behavior of other nodes. Identifying dominant sets in a graph can also help in optimization problems, as it can help us find the minimum set of vertices that can control the entire graph. Now that there are theories about vagueness, it is important to define parallel ideas in vague structures, such as intuitionistic fuzzy graphs. This paper describes a better way to find dominating vertex sets (DVSs) in intuitive fuzzy graphs (IFGs). Even though there is already an algorithm for finding DVSs in IFGs, it has some problems. For example, it does not take into account the vertex volume, which has a direct effect on how DVSs are calculated. To address these limitations, we propose a new algorithm that can handle large-scale IFGs more efficiently. We show how effective and scalable the method is by comparing it to other methods and applying it to water flow. This work’s contributions can be used in many areas, such as social network analysis, transportation planning, and telecommunications. Full article
(This article belongs to the Special Issue Fuzzy Systems and Decision Making Theory)
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