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New Trend on Fuzzy Systems and Intelligent Decision Making Theory:...
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New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 46111

Special Issue Editors

Prof. Dr. Luis Martínez López

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Guest Editor
Department of Computer Science, University of Jaén, 23071 Jaén, Spain
Interests: computational intelligence; fuzzy decision making; recommender systems
Special Issues, Collections and Topics in MDPI journals
Dr. Lesheng Jin

E-Mail Website
Guest Editor
Business School, Nanjing Normal University, Nanjing, China
Interests: preference involved decision making; linguistic decision making; information fusion; aggregation operators
Special Issues, Collections and Topics in MDPI journals
Dr. Zhen-Song Chen

E-Mail Website
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
Prof. Dr. Humberto Bustince

E-Mail Website
Guest Editor
Department of Statistics, Computer Science and Mathematics, Public University of Navarra, 31006 Pamplona, Spain
Interests: aggregation functions; theoretical aspects of fuzzy sets and their extensions; image processing; classification; decision making; bio-inspired algorithms; partial differential equations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to Dr. Ronald R. Yager’s research; he has been and still is a pioneer in several fields including fuzzy systems and intelligent decision-making theory, among others. Dr. Yager is one of the most esteemed and highly cited researchers in the field of computational intelligence and won several awards including IEEE Computational Intelligence Society Fuzzy Systems Pioneer Award (2004), IEEE Outstanding Contributor Award Granular Computing (2006), IEEE Frank Rosenblatt Award (2016), and Lotfi A. Zadeh Pioneer Award of the IEEE Systems, Man, and Cybernetics Society (2018), etc. He is the founder and former editor in chief (1988–2020) of the International Journal of Intelligent Systems. He has published many papers, most of which were written independently by himself, and many books/monographs.

To honor his contributions, this Special Issue will gather research works and reviews from researchers and scientists working in the research areas most impacted by Dr. Yager’s research. The Special Issue welcomes original contributions that advance the state-of-the-art of intelligent decision-making theory, preference and uncertainty involved decision making, fuzzy systems, extended fuzzy sets with application, information fusion techniques, and other related topics.

Prof. Dr. Luis Martínez López
Dr. Lesheng Jin
Dr. Zhen-Song Chen
Prof. Dr. Humberto Bustince
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • aggregation operators
  • basic uncertain information
  • data aggregation in pattern recognition and image processing
  • data aggregation in fuzzy reasoning
  • decision theory
  • decision science
  • explainable artificial intelligence
  • extended fuzzy sets
  • fuzzy decision making
  • fuzzy logic
  • fuzzy measures with application
  • fuzzy systems
  • granular computing
  • group decision making
  • information fusion
  • intelligent decision-making in computer sciences
  • intelligent decision-making in operations research
  • intelligent decision-making in management sciences
  • intelligent decision-making in economic theory
  • intelligent decision-making related fuzzy modeling
  • intelligent decision-making in recommender systems
  • interval valued aggregation
  • linguistic aggregation fusion and decision making
  • multi-criteria decision-making and evaluation
  • ordered weighted averaging operators
  • preference involved decision-making
  • rules-based decision-making
  • t-norm logic
  • uncertain aggregation fusion
  • uncertainty-involved decision-making

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Published Papers (18 papers)

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15 pages, 1197 KiB  
Article
A Novel Method Based on the Fuzzy Entropy Measure to Optimize the Fuzziness in Trapezoidal Strong Fuzzy Partitions
by Barbara Cardone and Ferdinando Di Martino
Information 2024, 15(10), 615; https://doi.org/10.3390/info15100615 - 7 Oct 2024
Viewed by 476
Abstract
Analyzing the uncertainty of outcomes based on estimates of the data’s membership degrees to fuzzy sets is essential for making decisions. These fuzzy sets are often designated by experts as strong fuzzy partitions of the data domain with trapezoidal fuzzy numbers. Some indices [...] Read more.
Analyzing the uncertainty of outcomes based on estimates of the data’s membership degrees to fuzzy sets is essential for making decisions. These fuzzy sets are often designated by experts as strong fuzzy partitions of the data domain with trapezoidal fuzzy numbers. Some indices of the fuzzy set’s fuzziness provide an assessment of the degree of uncertainty of the results. It is feasible to bring the fuzzy sets’ fuzziness below a tolerable level by suitably redefining the strong fuzzy partition. Significant differences in the original fuzzy partition, however, result in disparities concerning the decision maker’s approximative reasoning and the interpretability of the results. In light of this, we provide in this study a technique applied to trapezoidal strong fuzzy partitions that, while not appreciably altering the original fuzzy partition, reduces the fuzziness of its fuzzy sets. The fuzziness of the fuzzy sets is assessed using the De Luca and Termini fuzzy entropy. An iterative process is then executed, with the aim of modifying the cores of the trapezoidal fuzzy partitions to decrease their fuzziness. This technique is tested on datasets containing average daily temperatures measured in various cities. The findings demonstrate that this approach strikes a great balance between the goal of lessening the fuzziness of the fuzzy sets and the goal of not appreciably altering the original fuzzy partition. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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12 pages, 746 KiB  
Article
Intuitionistic Fuzzy Sets for Spatial and Temporal Data Intervals
by Frederick Petry
Information 2024, 15(4), 240; https://doi.org/10.3390/info15040240 - 20 Apr 2024
Viewed by 1054
Abstract
Spatial and temporal uncertainties are found in data for many critical applications. This paper describes the use of interval-based representations of some spatial and temporal information. Uncertainties in the information can arise from multiple sources in which degrees of support and non-support occur [...] Read more.
Spatial and temporal uncertainties are found in data for many critical applications. This paper describes the use of interval-based representations of some spatial and temporal information. Uncertainties in the information can arise from multiple sources in which degrees of support and non-support occur in evaluations. This motivates the use of intuitionistic fuzzy sets to permit the use of the positive and negative memberships to capture these uncertainties. The interval representations will include both simple and complex or nested intervals. The relationships between intervals such as overlapping, containing, etc. are then developed for both the simple and complex intervals. Such relationships are required to support the aggregation approaches of the interval information. Both averaging and merging approaches to interval aggregation are then developed. Furthermore, potential techniques for the associated aggregation of the interval intuitionistic fuzzy memberships are provided. A motivating example of maritime depth data required for safe navigation is used to illustrate the approach. Finally, some potential future developments are discussed. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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14 pages, 1081 KiB  
Article
Ensemble Modeling with a Bayesian Maximal Information Coefficient-Based Model of Bayesian Predictions on Uncertainty Data
by Tisinee Surapunt and Shuliang Wang
Information 2024, 15(4), 228; https://doi.org/10.3390/info15040228 - 18 Apr 2024
Cited by 1 | Viewed by 1453
Abstract
Uncertainty presents unfamiliar circumstances or incomplete information that may be difficult to handle with a single model of a traditional machine learning algorithm. They are possibly limited by inadequate data, an ambiguous model, and learning performance to make a prediction. Therefore, ensemble modeling [...] Read more.
Uncertainty presents unfamiliar circumstances or incomplete information that may be difficult to handle with a single model of a traditional machine learning algorithm. They are possibly limited by inadequate data, an ambiguous model, and learning performance to make a prediction. Therefore, ensemble modeling is proposed as a powerful model for enhancing predictive capabilities and robustness. This study aims to apply Bayesian prediction to ensemble modeling because it can encode conditional dependencies between variables and present the reasoning model using the BMIC model. The BMIC has clarified knowledge in the model which is ready for learning. Then, it was selected as the base model to be integrated with well-known algorithms such as logistic regression, K-nearest neighbors, decision trees, random forests, support vector machines (SVMs), neural networks, naive Bayes, and XGBoost classifiers. Also, the Bayesian neural network (BNN) and the probabilistic Bayesian neural network (PBN) were considered to compare their performance as a single model. The findings of this study indicate that the ensemble model of the BMIC with some traditional algorithms, which are SVM, random forest, neural networks, and XGBoost classifiers, returns 96.3% model accuracy in prediction. It provides a more reliable model and a versatile approach to support decision-making. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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18 pages, 312 KiB  
Article
Large-Scale Group Decision-Making Method Using Hesitant Fuzzy Rule-Based Network for Asset Allocation
by Abdul Malek Yaakob, Shahira Shafie, Alexander Gegov, Siti Fatimah Abdul Rahman and Ku Muhammad Naim Ku Khalif
Information 2023, 14(11), 588; https://doi.org/10.3390/info14110588 - 26 Oct 2023
Viewed by 1359
Abstract
Large-scale group decision-making (LSGDM) has become common in the new era of technology development involving a large number of experts. Recently, in the use of social network analysis (SNA), the community detection method has been highlighted by researchers as a useful method in [...] Read more.
Large-scale group decision-making (LSGDM) has become common in the new era of technology development involving a large number of experts. Recently, in the use of social network analysis (SNA), the community detection method has been highlighted by researchers as a useful method in handling the complexity of LSGDM. However, it is still challenging to deal with the reliability and hesitancy of information as well as the interpretability of the method. For this reason, we introduce a new approach of a Z-hesitant fuzzy network with the community detection method being put into practice for stock selection. The proposed approach was subsequently compared to an established approach in order to evaluate its applicability and efficacy. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
18 pages, 3382 KiB  
Article
Employee Productivity Assessment Using Fuzzy Inference System
by Mohammad Nikmanesh, Ardalan Feili and Shahryar Sorooshian
Information 2023, 14(7), 423; https://doi.org/10.3390/info14070423 - 22 Jul 2023
Cited by 5 | Viewed by 2709
Abstract
The success of an organization hinges upon the effective utilization of its human resources, which serves as a crucial developmental factor and competitive advantage, and sets the organization apart from others. Evaluating staff productivity involves considering various dimensions, notably structural, behavioral, and circumferential [...] Read more.
The success of an organization hinges upon the effective utilization of its human resources, which serves as a crucial developmental factor and competitive advantage, and sets the organization apart from others. Evaluating staff productivity involves considering various dimensions, notably structural, behavioral, and circumferential factors. These factors collectively form a three-pronged model that comprehensively encompasses the facets of an organization. However, assessing the productivity of employees poses challenges, due to the inherent complexity of the humanities domain. Fuzzy logic offers a sound approach to address this issue, employing its rationale and leveraging a fuzzy inference system (FIS) as a sophisticated toolbox for measuring productivity. Fuzzy inference systems enhance the flexibility, speed, and adaptability in soft computation. Likewise, their applications, integration, hybridization, and adaptation are also introduced. They also provide an alternative solution to deal with imprecise data. In this study, we endeavored to identify and measure the productivity of human resources within a case study, by developing an alternative framework known as an FIS. Our findings provided evidence to support the validity of the alternative approach. Thus, the utilized approach for assessing employee productivity may provide managers and businesses with a more realistic asset. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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16 pages, 304 KiB  
Article
Data Mining Using Association Rules for Intuitionistic Fuzzy Data
by Frederick Petry and Ronald Yager
Information 2023, 14(7), 372; https://doi.org/10.3390/info14070372 - 29 Jun 2023
Viewed by 1398
Abstract
This paper considers approaches to the computation of association rules for intuitionistic fuzzy data. Association rules can provide guidance for assessing the significant relationships that can be determined while analyzing data. The approach uses the cardinality of intuitionistic fuzzy sets that provide a [...] Read more.
This paper considers approaches to the computation of association rules for intuitionistic fuzzy data. Association rules can provide guidance for assessing the significant relationships that can be determined while analyzing data. The approach uses the cardinality of intuitionistic fuzzy sets that provide a minimum and maximum range for the support and confidence metrics. A new notation is used to enable the representation of the fuzzy metrics. A running example of queries about the desirable features of vacation locations is used to illustrate. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
24 pages, 2972 KiB  
Article
Fermatean Fuzzy-Based Personalized Prioritization of Barriers to IoT Adoption within the Clean Energy Context
by N Sai Snehitha Reddy, Raghunathan Krishankumar, S Shanmugha Priya, Fausto Cavallaro, Abbas Mardani and Kattur Soundarapandian Ravichandran
Information 2023, 14(6), 309; https://doi.org/10.3390/info14060309 - 29 May 2023
Cited by 3 | Viewed by 1671
Abstract
Globally, industries are focusing on green habits, with world leaders demanding net zero carbon; clean energy is considered an attractive and viable option. The Internet of things (IoT) is an emerging technology with potential opportunities in the clean energy domain for quality improvement [...] Read more.
Globally, industries are focusing on green habits, with world leaders demanding net zero carbon; clean energy is considered an attractive and viable option. The Internet of things (IoT) is an emerging technology with potential opportunities in the clean energy domain for quality improvement in production and management. Earlier studies on IoTs show evidence that direct adoption of such digital technology is an ordeal and incurs adoption barriers that must be prioritized for effective management. Motivated by the claim, in this paper, the authors attempt to prioritize the diverse adoption barriers with the support of the newly proposed Fermatean fuzzy-based decision framework. Initially, qualitative rating information is collected via questionnaires on barriers and criteria from the circular economy (CE). Later, these are converted to Fermatean fuzzy numbers used by integrated approaches for decision processes. A regret scheme is put forward for determining CE criteria importance, and the barriers are prioritized by using a novel ranking algorithm that incorporates the WASPAS formulation and experts’ personal choices during rank estimation. The applicability of the developed framework is testified via a case example. Sensitivity analysis and comparison reveal the merits and limitations of the developed decision model. Results show that labor/workforce skill insufficiency, an ineffective framework for performance, a technology divide, insufficient legislation and control, and lack of time for training and skill practice are the top five barriers that hinder IoT adoption, based on the rating data. Additionally, the criteria such as cost cutting via a reuse scheme, resource circularity, emission control, and scaling profit with green habits are the top four criteria for their relative importance values. From these inferences, the respective authorities in the clean energy sector could effectively plan their strategies for addressing these barriers to promote IoT adoption in the clean energy sector. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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21 pages, 461 KiB  
Article
Generalized Frame for Orthopair Fuzzy Sets: (m,n)-Fuzzy Sets and Their Applications to Multi-Criteria Decision-Making Methods
by Tareq M. Al-shami and Abdelwaheb Mhemdi
Information 2023, 14(1), 56; https://doi.org/10.3390/info14010056 - 16 Jan 2023
Cited by 60 | Viewed by 3247
Abstract
Orthopairs (pairs of disjoint sets) have points in common with many approaches to managing vaguness/uncertainty such as fuzzy sets, rough sets, soft sets, etc. Indeed, they are successfully employed to address partial knowledge, consensus, and borderline cases. One of the generalized versions of [...] Read more.
Orthopairs (pairs of disjoint sets) have points in common with many approaches to managing vaguness/uncertainty such as fuzzy sets, rough sets, soft sets, etc. Indeed, they are successfully employed to address partial knowledge, consensus, and borderline cases. One of the generalized versions of orthopairs is intuitionistic fuzzy sets which is a well-known theory for researchers interested in fuzzy set theory. To extend the area of application of fuzzy set theory and address more empirical situations, the limitation that the grades of membership and non-membership must be calibrated with the same power should be canceled. To this end, we dedicate this manuscript to introducing a generalized frame for orthopair fuzzy sets called “(m,n)-Fuzzy sets”, which will be an efficient tool to deal with issues that require different importances for the degrees of membership and non-membership and cannot be addressed by the fuzzification tools existing in the published literature. We first establish its fundamental set of operations and investigate its abstract properties that can then be transmitted to the various models they are in connection with. Then, to rank (m,n)-Fuzzy sets, we define the functions of score and accuracy, and formulate aggregation operators to be used with (m,n)-Fuzzy sets. Ultimately, we develop the successful technique “aggregation operators” to handle multi-criteria decision-making problems in the environment of (m,n)-Fuzzy sets. The proposed technique has been illustrated and analyzed via a numerical example. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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16 pages, 1588 KiB  
Article
Wavelet-Based Classification of Enhanced Melanoma Skin Lesions through Deep Neural Architectures
by Premaladha Jayaraman, Nirmala Veeramani, Raghunathan Krishankumar, Kattur Soundarapandian Ravichandran, Fausto Cavallaro, Pratibha Rani and Abbas Mardani
Information 2022, 13(12), 583; https://doi.org/10.3390/info13120583 - 15 Dec 2022
Cited by 7 | Viewed by 1979
Abstract
In recent years, skin cancer diagnosis has been aided by the most sophisticated and advanced machine learning algorithms, primarily implemented in the spatial domain. In this research work, we concentrated on two crucial phases of a computer-aided diagnosis system: (i) image enhancement through [...] Read more.
In recent years, skin cancer diagnosis has been aided by the most sophisticated and advanced machine learning algorithms, primarily implemented in the spatial domain. In this research work, we concentrated on two crucial phases of a computer-aided diagnosis system: (i) image enhancement through enhanced median filtering algorithms based on the range method, fuzzy relational method, and similarity coefficient, and (ii) wavelet decomposition using DB4, Symlet, RBIO, and extracting seven unique entropy features and eight statistical features from the segmented image. The extracted features were then normalized and provided for classification based on supervised and deep-learning algorithms. The proposed system is comprised of enhanced filtering algorithms, Normalized Otsu’s Segmentation, and wavelet-based entropy. Statistical feature extraction led to a classification accuracy of 93.6%, 0.71% higher than the spatial domain-based classification. With better classification accuracy, the proposed system will assist clinicians and dermatology specialists in identifying skin cancer early in its stages. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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13 pages, 6602 KiB  
Article
Explainable Decision-Making for Water Quality Protection
by Jozo Dujmović and William L. Allen III
Information 2022, 13(12), 551; https://doi.org/10.3390/info13120551 - 23 Nov 2022
Cited by 1 | Viewed by 1145
Abstract
All professional decisions prepared for a specific stakeholder can and must be explained. The primary role of explanation is to defend and reinforce the proposed decision, supporting stakeholder confidence in the validity of the decision. In this paper we present the methodology for [...] Read more.
All professional decisions prepared for a specific stakeholder can and must be explained. The primary role of explanation is to defend and reinforce the proposed decision, supporting stakeholder confidence in the validity of the decision. In this paper we present the methodology for explaining results of the evaluation of alternatives for water quality protection for a real-life project, the Upper Neuse Clean Water Initiative in North Carolina. The evaluation and comparison of alternatives is based on the Logic Scoring of Preference (LSP) method. We identify three explainability problems: (1) the explanation of LSP criterion properties, (2) the explanation of evaluation results for each alternative, and (3) the explanation of the comparison and ranking of alternatives. To solve these problems, we introduce a set of explainability indicators that characterize properties that are necessary for verbal explanations that humans can understand. In addition, we use this project to show the methodology for automatic generation of explainability reports. We recommend the use of explainability reports as standard supplements for evaluation reports containing the results of evaluation projects based on the LSP method. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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17 pages, 911 KiB  
Article
Accelerating Update of Variable Precision Multigranulation Approximations While Adding Granular Structures
by Changchun Li and Chengxiang Hu
Information 2022, 13(11), 541; https://doi.org/10.3390/info13110541 - 15 Nov 2022
Viewed by 1394
Abstract
In multigranulation environments, variable precision multigranulation rough set (VPMGRS) is a useful framework that has a tolerance for errors. Approximations are basic concepts for knowledge acquisition and attribute reductions. Accelerating update of approximations can enhance the efficiency of acquiring decision rules by utilizing [...] Read more.
In multigranulation environments, variable precision multigranulation rough set (VPMGRS) is a useful framework that has a tolerance for errors. Approximations are basic concepts for knowledge acquisition and attribute reductions. Accelerating update of approximations can enhance the efficiency of acquiring decision rules by utilizing previously saved information. In this study, we focus on exploiting update mechanisms of approximations in VPMGRS with the addition of granular structures. By analyzing the basic changing trends of approximations in VPMGRS, we develop accelerating update mechanisms for acquiring approximations. In addition, an incremental algorithm to update variable precision multigranulation approximations is proposed when adding multiple granular structures. Finally, extensive comparisons elaborate the efficiency of the incremental algorithm. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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13 pages, 298 KiB  
Article
An Iterative Approach for the Solution of the Constrained OWA Aggregation Problem with Two Comonotone Constraints
by Lucian Coroianu and Robert Fullér
Information 2022, 13(10), 443; https://doi.org/10.3390/info13100443 - 21 Sep 2022
Viewed by 1137
Abstract
In this paper, first, we extend the analytical expression of the optimal solution of the constrained OWA aggregation problem with two comonotone constraints by also including the case when the OWA weights are arbitrary non-negative numbers. Then, we indicate an iterative algorithm that [...] Read more.
In this paper, first, we extend the analytical expression of the optimal solution of the constrained OWA aggregation problem with two comonotone constraints by also including the case when the OWA weights are arbitrary non-negative numbers. Then, we indicate an iterative algorithm that precisely indicates whether a constraint in an auxiliary problem is either biding or strictly redundant. Actually, the biding constraint (or two biding constraints, as this case also may occur) are essential in expressing the solution of the initial constrained OWA aggregation problem. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
17 pages, 2481 KiB  
Article
Logarithmic Negation of Basic Probability Assignment and Its Application in Target Recognition
by Shijun Xu, Yi Hou, Xinpu Deng, Peibo Chen and Shilin Zhou
Information 2022, 13(8), 387; https://doi.org/10.3390/info13080387 - 15 Aug 2022
Cited by 1 | Viewed by 1715
Abstract
The negation of probability distribution is a new perspective from which to obtain information. Dempster–Shafer (D–S) evidence theory, as an extension of possibility theory, is widely used in decision-making-level fusion. However, how to reasonably construct the negation of basic probability assignment (BPA) in [...] Read more.
The negation of probability distribution is a new perspective from which to obtain information. Dempster–Shafer (D–S) evidence theory, as an extension of possibility theory, is widely used in decision-making-level fusion. However, how to reasonably construct the negation of basic probability assignment (BPA) in D–S evidence theory is an open issue. This paper proposes a new negation of BPA, logarithmic negation. It solves the shortcoming of Yin’s negation that maximal entropy cannot be obtained when there are only two focal elements in the BPA. At the same time, the logarithmic negation of BPA inherits the good properties of the negation of probability, such as order reversal, involution, convergence, degeneration, and maximal entropy. Logarithmic negation degenerates into Gao’s negation when the values of the elements all approach 0. In addition, the data fusion method based on logarithmic negation has a higher belief value of the correct target in target recognition application. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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15 pages, 634 KiB  
Article
Decision-Making Model for Reinforcing Digital Transformation Strategies Based on Artificial Intelligence Technology
by Kyungtae Kim and Boyoung Kim
Information 2022, 13(5), 253; https://doi.org/10.3390/info13050253 - 13 May 2022
Cited by 17 | Viewed by 8039
Abstract
Firms’ digital environment changes and industrial competitions have evolved quickly since the Fourth Industrial Revolution and the COVID-19 pandemic. Many companies are propelling company-wide digital transformation strategies based on artificial intelligence (AI) technology for the digital innovation of organizations and businesses. This study [...] Read more.
Firms’ digital environment changes and industrial competitions have evolved quickly since the Fourth Industrial Revolution and the COVID-19 pandemic. Many companies are propelling company-wide digital transformation strategies based on artificial intelligence (AI) technology for the digital innovation of organizations and businesses. This study aims to define the factors affecting digital transformation strategies and present a decision-making model required for digital transformation strategies based on the definition. It also reviews previous AI technology and digital transformation strategies and draws influence factors. The research model drew four evaluation areas, such as subject, environment, resource, and mechanism, and 16 evaluation factors through the SERM model. After the factors were reviewed through the Delphi methods, a questionnaire survey was conducted targeting experts with over 10 years of work experience in the digital strategy field. The study results were produced by comparing the data’s importance using an Analytic Hierarchy Process (AHP) on each group. According to the analysis, the subject was the most critical factor, and the CEO (top management) was more vital than the core talent or technical development organization. The importance was shown in the order of resource, mechanism and environment, following subject. It was ascertained that there were differences of importance in industrial competition and market digitalization in the demander and provider groups. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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21 pages, 512 KiB  
Article
Text Classification Using Intuitionistic Fuzzy Set Measures—An Evaluation Study
by George K. Sidiropoulos, Nikolaos Diamianos, Kyriakos D. Apostolidis and George A. Papakostas
Information 2022, 13(5), 235; https://doi.org/10.3390/info13050235 - 5 May 2022
Cited by 4 | Viewed by 2646
Abstract
A very important task of Natural Language Processing is text categorization (or text classification), which aims to automatically classify a document into categories. This kind of task includes numerous applications, such as sentiment analysis, language or intent detection, heavily used by social-/brand-monitoring tools, [...] Read more.
A very important task of Natural Language Processing is text categorization (or text classification), which aims to automatically classify a document into categories. This kind of task includes numerous applications, such as sentiment analysis, language or intent detection, heavily used by social-/brand-monitoring tools, customer service, and the voice of customer, among others. Since the introduction of Fuzzy Set theory, its application has been tested in many fields, from bioinformatics to industrial and commercial use, as well as in cases with vague, incomplete, or imprecise data, highlighting its importance and usefulness in the fields. The most important aspect of the application of Fuzzy Set theory is the measures employed to calculate how similar or dissimilar two samples in a dataset are. In this study, we evaluate the performance of 43 similarity and 19 distance measures in the task of text document classification, using the widely used BBC News and BBC Sports benchmark datasets. Their performance is optimized through hyperparameter optimization techniques and evaluated via a leave-one-out cross-validation technique, presenting their performance using the accuracy, precision, recall, and F1-score metrics. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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8 pages, 237 KiB  
Article
Ordered Weighted Averaging (OWA), Decision Making under Uncertainty, and Deep Learning: How Is This All Related?
by Vladik Kreinovich
Information 2022, 13(2), 82; https://doi.org/10.3390/info13020082 - 11 Feb 2022
Viewed by 3977
Abstract
Among many research areas to which Ron Yager contributed are decision making under uncertainty (in particular, under interval and fuzzy uncertainty) and aggregation—where he proposed, analyzed, and utilized ordered weighted averaging (OWA). The OWA algorithm itself provides only a specific type of data [...] Read more.
Among many research areas to which Ron Yager contributed are decision making under uncertainty (in particular, under interval and fuzzy uncertainty) and aggregation—where he proposed, analyzed, and utilized ordered weighted averaging (OWA). The OWA algorithm itself provides only a specific type of data aggregation. However, it turns out that if we allow several OWA stages, one after another, we obtain a scheme with a universal approximation property—moreover, a scheme which is perfectly equivalent to modern ReLU-based deep neural networks. In this sense, Ron Yager can be viewed as a (grand)father of ReLU-based deep learning. We also recall that the existing schemes for decision making under uncertainty are also naturally interpretable in OWA terms. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
10 pages, 1275 KiB  
Article
Fsmpy: A Fuzzy Set Measures Python Library
by George K. Sidiropoulos, Kyriakos D. Apostolidis, Nikolaos Damianos and George A. Papakostas
Information 2022, 13(2), 64; https://doi.org/10.3390/info13020064 - 27 Jan 2022
Cited by 3 | Viewed by 3975
Abstract
This paper presents the fsmpy Python library for the implementation of any type of measures and comparisons of different types of fuzzy sets, as well as other important and useful utilities and algorithms. In this paper, we analyze the motivation behind its implementation, [...] Read more.
This paper presents the fsmpy Python library for the implementation of any type of measures and comparisons of different types of fuzzy sets, as well as other important and useful utilities and algorithms. In this paper, we analyze the motivation behind its implementation, the design principles followed, the implemented modules of the library and its capabilities, considering intuitionistic fuzzy sets as the case study. Lastly, some examples of its application to widely used pattern recognition, medical diagnosis and image segmentation are presented. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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Review

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24 pages, 2082 KiB  
Review
The Application of Z-Numbers in Fuzzy Decision Making: The State of the Art
by Nik Muhammad Farhan Hakim Nik Badrul Alam, Ku Muhammad Naim Ku Khalif, Nor Izzati Jaini and Alexander Gegov
Information 2023, 14(7), 400; https://doi.org/10.3390/info14070400 - 13 Jul 2023
Cited by 11 | Viewed by 2901
Abstract
A Z-number is very powerful in describing imperfect information, in which fuzzy numbers are paired such that the partially reliable information is properly processed. During a decision-making process, human beings always use natural language to describe their preferences, and the decision information is [...] Read more.
A Z-number is very powerful in describing imperfect information, in which fuzzy numbers are paired such that the partially reliable information is properly processed. During a decision-making process, human beings always use natural language to describe their preferences, and the decision information is usually imprecise and partially reliable. The nature of the Z-number, which is composed of the restriction and reliability components, has made it a powerful tool for depicting certain decision information. Its strengths and advantages have attracted many researchers worldwide to further study and extend its theory and applications. The current research trend on Z-numbers has shown an increasing interest among researchers in the fuzzy set theory, especially its application to decision making. This paper reviews the application of Z-numbers in decision making, in which previous decision-making models based on Z-numbers are analyzed to identify their strengths and contributions. The decision making based on Z-numbers improves the reliability of the decision information and makes it more meaningful. Another scope that is closely related to decision making, namely, the ranking of Z-numbers, is also reviewed. Then, the evaluative analysis of the Z-numbers is conducted to evaluate the performance of Z-numbers in decision making. Future directions and recommendations on the applications of Z-numbers in decision making are provided at the end of this review. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A new Large scale group decision making model based on Hesitant fuzzy network and Social network analysis for Asset allocation
Authors: Alexander Gegov; Abdul Malek bin Yaakob; KU MUHAMMAD NA'IM BIN KU KHALIF
Affiliation: --
Abstract: Fuzzy systems are frequently used to solve decision-making problems that require numerous criteria to select the best options. The end outcomes of the fuzzy system, on the other hand, demonstrate no linking of the intermediate criteria involved, resulting in low certainty and trust in the conclusion. Fuzzy system can be demonstrated in the single and multiple rule based. Both offer moderate level of certainty and transparency in dealing with complex decision-making. Thus, fuzzy network, a networked rule-based system delivers transparency when explicitly including the interior structure of the demonstrating network by depicting the arrangement of measures as a node and the network. This paper serves transparency through fuzzy network approach in the formulation and incorporating social network analysis in dealing with complexity of large scale in decision making. Reliability and hesitancy are included in the fuzzy network approach through the application of Z-numbers and hesitant fuzzy sets. Z-numbers are implemented through the evaluations of decision makers as decision makers will decide on their confidence level and hesitant fuzzy set is subsumed in fuzzy network in order to allow decision makers to incorporate several possible evaluations. This method is verified through the comparison of proposed method to established fuzzy methods under Spearman rank correlation method. it is observed that the novel method namely Z-Hesitant Fuzzy Network with Social Network Analysis approach outperform the established LGDM TOPSIS methods. In conclusion, the constructed method have significant contribution to the application of fuzzy system theory in decision making environment. The model has been registered under copyright reference number LY2022P02004.

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