The Experts below are selected from a list of 5082 Experts worldwide ranked by ideXlab platform
Luis Martinez - One of the best experts on this subject based on the ideXlab platform.
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A hesitant fuzzy Linguistic Model for emergency decision making based on fuzzy TODIM method
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017Co-Authors: Liang Wang, Rosa M. Rodríguez, Ying-ming Wang, Luis MartinezAbstract:The importance of emergency decision making (EDM) has grown up in recent years because of the frequent occurrence of multiple emergency events (EEs) that have caused important social and economic losses. EDM plays a relevant role when it is necessary to mitigate property and lives losses and reducing the negative impacts on the social and environmental development. Real-world EDM problems are usually characterized by complexity, hard time constraints, lack of information and the impact of the psychological behaviors which makes it very challenging task for the decision maker. This characterization shows the need of dealing with different types of uncertainty and the managing of behaviors to face these problems. This contribution proposes a new emergency decision Model that first, uses fuzzy Linguistic information to Model the subjective information elicited by the decision maker under uncertainty and also the Modelling of his/her hesitancy for assessing his/her judgements by using hesitant fuzzy Linguistic term sets. Second it integrates the decision maker's psychological behavior by using the prospect theory in a fuzzy based environment. Finally, an example of application of the decision Model is carried out to show its validity and applicability.
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2-Tuple Linguistic Model
The 2-tuple Linguistic Model, 2015Co-Authors: Luis Martinez, Rosa M. Rodríguez, Francisco HerreraAbstract:The main concepts of Computing with Words and Linguistic decision making (LDM) have been reviewed and some limitations of the classical Linguistic computational Models dealing with Linguistic information pointed out. This chapter introduces the aim, concept, representation, notation, and transformation functions needed to deal with the 2-tuple Linguistic Model that are the basis for developing an accurate symbolic computational Model defined on this representation for Computing with Words in LDM. Several basic operators are then defined, paying special attention to the aggregation ones due to their relevance in LDM.
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flintstones a fuzzy Linguistic decision tools enhancement suite based on the 2 tuple Linguistic Model and extensions
Information Sciences, 2014Co-Authors: Francisco J. Estrella, Francisco Herrera, Macarena Espinilla, Luis MartinezAbstract:Uncertainty in real world decision making problems not always has probabilistic nature, in such cases the use of Linguistic information to Model and manage such an uncertainty has given good results. The adoption of Linguistic information implies the accomplishment of processes of computing with words to solve Linguistic decision making problems. In the specialized literature, several computational Models can be found to carry out such processes. However, there is a shortage of software tools that develop and implement these computational Models. The 2-tuple Linguistic Model has been widely used to operate with Linguistic information in decision problems due to the fact that provides Linguistic results that are accurate and easy to understand for human beings. Furthermore, another advantage of the 2-tuple Linguistic Model is the existence of different extensions to accomplish processes of computing with words in complex decision frameworks. Due to these reasons, in this paper a fuzzy Linguistic decision tools enhancement suite so-called Flintstones is proposed to solve Linguistic decision making problems based on the 2-tuple Linguistic Model and its extensions. Additionally, the Flintstones website is also presented, this website has been deployed and includes a repository of case studies and datasets for different Linguistic decision making problems. Finally, a case study solved by Flintstones is illustrated in order to show its performance, usefulness and effectiveness.
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On the use of Hesitant Fuzzy Linguistic Term Set in FLINTSTONES
2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014Co-Authors: Francisco J. Estrella, Macarena Espiniila, Rosa M. Rodríguez, Luis MartinezAbstract:The use of Linguistic information to Model and manage uncertainty in Decision Making (DM) has been a key subject of many proposals in the literature. The 2-tuple Linguistic Model and its extensions in Linguistic DM has been very successful and extensive due to their flexibility and accuracy. Flintstones is a novel fuzzy Linguistic decision tool enhancement suite that implements tools to facilitate the solving of Linguistic DM problems that Model the Linguistic information with such a Model and its extensions. However, both the 2-tuple Linguistic Model and Flintstones can not deal with uncertain situations Modelled Linguistically in which experts hesitate among several Linguistic terms. For these cases, recently, it has been proposed the use of Hesitant Fuzzy Linguistic Term Sets (HFLTS) that have attracted a lot of research interest, mainly regarding its application in DM. Hence in this contribution it is proposed an extended version of Flintstones that includes the ability and functionality of dealing with HFLTS in Linguistic decision problems and enables the integration, validity and performance of hesitant Linguistic decision Models and operators.
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FUZZ-IEEE - On the use of Hesitant Fuzzy Linguistic Term Set in FLINTSTONES
2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014Co-Authors: Francisco J. Estrella, Macarena Espiniila, Rosa M. Rodríguez, Luis MartinezAbstract:The use of Linguistic information to Model and manage uncertainty in Decision Making (DM) has been a key subject of many proposals in the literature. The 2-tuple Linguistic Model and its extensions in Linguistic DM has been very successful and extensive due to their flexibility and accuracy. Flintstones is a novel fuzzy Linguistic decision tool enhancement suite that implements tools to facilitate the solving of Linguistic DM problems that Model the Linguistic information with such a Model and its extensions. However, both the 2-tuple Linguistic Model and Flintstones can not deal with uncertain situations Modelled Linguistically in which experts hesitate among several Linguistic terms. For these cases, recently, it has been proposed the use of Hesitant Fuzzy Linguistic Term Sets (HFLTS) that have attracted a lot of research interest, mainly regarding its application in DM. Hence in this contribution it is proposed an extended version of Flintstones that includes the ability and functionality of dealing with HFLTS in Linguistic decision problems and enables the integration, validity and performance of hesitant Linguistic decision Models and operators.
Francisco Herrera - One of the best experts on this subject based on the ideXlab platform.
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2-Tuple Linguistic Model
The 2-tuple Linguistic Model, 2015Co-Authors: Luis Martinez, Rosa M. Rodríguez, Francisco HerreraAbstract:The main concepts of Computing with Words and Linguistic decision making (LDM) have been reviewed and some limitations of the classical Linguistic computational Models dealing with Linguistic information pointed out. This chapter introduces the aim, concept, representation, notation, and transformation functions needed to deal with the 2-tuple Linguistic Model that are the basis for developing an accurate symbolic computational Model defined on this representation for Computing with Words in LDM. Several basic operators are then defined, paying special attention to the aggregation ones due to their relevance in LDM.
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the 2 tuple Linguistic Model computing with words in decision making
2015Co-Authors: Luis Martnez, Rosa M. Rodríguez, Francisco HerreraAbstract:This book examines one of the more common and wide-spread methodologies to deal with uncertainty in real-world decision making problems, the computing with words paradigm, and the fuzzy Linguistic approach. The 2-tuple Linguistic Model is the most popular methodology for computing with words (CWW), because it improves the accuracy of the Linguistic computations and keeps the interpretability of the results. The authors provide a thorough review of the specialized literature in CWW and highlight the rapid growth and applicability of the 2-tuple Linguistic Model. They explore the foundations and methodologies for CWW in complex frameworks and extensions. The book introduces the software FLINTSTONES that provides tools for solving Linguistic decision problems based on the 2-tuple Linguistic Model. Professionals and researchers working in the field of classification or fuzzy sets and systems will find The 2-tuple Linguistic Model: Computing with Words in Decision Making a valuable resource. Undergraduate and postdoctoral students studying computer science and statistics will also find this book a useful study guide.
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flintstones a fuzzy Linguistic decision tools enhancement suite based on the 2 tuple Linguistic Model and extensions
Information Sciences, 2014Co-Authors: Francisco J. Estrella, Francisco Herrera, Macarena Espinilla, Luis MartinezAbstract:Uncertainty in real world decision making problems not always has probabilistic nature, in such cases the use of Linguistic information to Model and manage such an uncertainty has given good results. The adoption of Linguistic information implies the accomplishment of processes of computing with words to solve Linguistic decision making problems. In the specialized literature, several computational Models can be found to carry out such processes. However, there is a shortage of software tools that develop and implement these computational Models. The 2-tuple Linguistic Model has been widely used to operate with Linguistic information in decision problems due to the fact that provides Linguistic results that are accurate and easy to understand for human beings. Furthermore, another advantage of the 2-tuple Linguistic Model is the existence of different extensions to accomplish processes of computing with words in complex decision frameworks. Due to these reasons, in this paper a fuzzy Linguistic decision tools enhancement suite so-called Flintstones is proposed to solve Linguistic decision making problems based on the 2-tuple Linguistic Model and its extensions. Additionally, the Flintstones website is also presented, this website has been deployed and includes a repository of case studies and datasets for different Linguistic decision making problems. Finally, a case study solved by Flintstones is illustrated in order to show its performance, usefulness and effectiveness.
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a proposal for the genetic lateral tuning of Linguistic fuzzy systems and its interaction with rule selection
IEEE Transactions on Fuzzy Systems, 2007Co-Authors: Rafael Alcala, Jesus Alcalafdez, Francisco HerreraAbstract:Linguistic fuzzy Modeling allows us to deal with the Modeling of systems by building a Linguistic Model which is clearly interpretable by human beings. However, since the accuracy and the interpretability of the obtained Model are contradictory properties, the necessity of improving the accuracy of the Linguistic Model arises when complex systems are Modeled. To solve this problem, one of the research lines in recent years has led to the objective of giving more accuracy to Linguistic fuzzy Modeling without losing the interpretability to a high level. In this paper, a new postprocessing approach is proposed to perform an evolutionary lateral tuning of membership functions, with the main aim of obtaining Linguistic Models with higher levels of accuracy while maintaining good interpretability. To do so, we consider a new rule representation scheme base on the Linguistic 2-tuples representation Model which allows the lateral variation of the involved labels. Furthermore, the cooperation of the lateral tuning together with fuzzy rule reduction mechanisms is studied in this paper, presenting results on different real applications. The obtained results show the good performance of the proposed approach in high-dimensional problems and its ability to cooperate with methods to remove unnecessary rules.
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a multigranular hierarchical Linguistic Model for design evaluation based on safety and cost analysis
International Journal of Intelligent Systems, 2005Co-Authors: Luis Martinez, Jianbo Yang, Francisco HerreraAbstract:Before implementing a design of a large engineering system different design proposals are evaluated. The information used by experts to evaluate different options may be vague and/or incomplete. Although different probabilistic tools and techniques have been used to deal with these kinds of problems, it seems better to use the fuzzy Linguistic approach to Model vagueness and the Dempster-Shafter theory of evidence for Modeling incompleteness and ignorance. In the evaluation of alternative designs, different criteria can be considered. In this article an evaluation process is developed in terms of Safety and Cost analysis. Both criteria involve uncertainty, vagueness, and ignorance due to their nature. Therefore, we propose an evaluation process defined in a Linguistic framework where both criteria will be conducted in different utility spaces, i.e., in a multigranular Linguistic domain. Once the evaluation framework has been defined, we present an evaluation process based on a Multi-Expert Multi-Criteria decision Model that will be able to deal with multigranular Linguistic information without loss of information in order to evaluate different design options for an engineering system in a precise manner. Accordingly, we propose the use of a multigranular Linguistic Model based on the Linguistic Hierarchies presented by Herrera and Martinez (“A Model based on Linguistic 2-tuples for dealing with multigranularity hierarchical Linguistic contexts in multi-expert decision-making.” IEEE Trans Syst Man Cybern B 2001;31(2):227–234). © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1161–1194, 2005.
Isis Truck - One of the best experts on this subject based on the ideXlab platform.
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towards an extension of the 2 tuple Linguistic Model to deal with unbalanced Linguistic term sets
arXiv: Artificial Intelligence, 2013Co-Authors: Mohammedamine Abchir, Isis TruckAbstract:In the domain of Computing with words (CW), fuzzy Linguistic approaches are known to be relevant in many decision-making problems. Indeed, they allow us to Model the human reasoning in replacing words, assessments, preferences, choices, wishes... by ad hoc variables, such as fuzzy sets or more sophisticated variables. This paper focuses on a particular Model: Herrera & Martinez' 2-tuple Linguistic Model and their approach to deal with unbalanced Linguistic term sets. It is interesting since the computations are accomplished without loss of information while the results of the decision-making processes always refer to the initial Linguistic term set. They propose a fuzzy partition which distributes data on the axis by using Linguistic hierarchies to manage the non-uniformity. However, the required input (especially the density around the terms) taken by their fuzzy partition algorithm may be considered as too much demanding in a real-world application, since density is not always easy to determine. Moreover, in some limit cases (especially when two terms are very closed semantically to each other), the partition doesn't comply with the data themselves, it isn't close to the reality. Therefore we propose to modify the required input, in order to offer a simpler and more faithful partition. We have added an extension to the package jFuzzyLogic and to the corresponding script language FCL. This extension supports both 2-tuple Models: Herrera & Martinez' and ours. In addition to the partition algorithm, we present two aggregation algorithms: the arithmetic means and the addition. We also discuss these kinds of 2-tuple Models.
Enrique Herreraviedma - One of the best experts on this subject based on the ideXlab platform.
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some interesting properties of the fuzzy Linguistic Model based on discrete fuzzy numbers to manage hesitant fuzzy Linguistic information
Applied Soft Computing, 2015Co-Authors: Juan Vicente Riera, Sebastia Massanet, Enrique Herreraviedma, Joan TorrensAbstract:Graphical abstractDisplay Omitted HighlightsProperties of the fuzzy Linguistic Model based on discrete fuzzy numbers are analysed.This Model is used to handle hesitant fuzzy Linguistic information.This Model includes the hesitant fuzzy Linguistic term sets Model.Some advantages of the Model based on discrete fuzzy numbers are pointed out.A fuzzy decision making Model based on discrete fuzzy numbers is proposed. The management of hesitant fuzzy information is a topic of special interest in fuzzy decision making. In this paper, we focus on the use and properties of the fuzzy Linguistic Modelling based on discrete fuzzy numbers to manage hesitant fuzzy Linguistic information. Among these properties, we can highlight the existence of aggregation functions with no need of transformations or the possibility of a greater flexibilization of the opinions of the experts, even using different Linguistic chains (multigranularity). Furthermore, based on these properties we perform a comparison between this Model and the one based on hesitant fuzzy Linguistic term sets, showing the advantages of the former with respect to the latter. Finally, a fuzzy decision making Model based on discrete fuzzy numbers is proposed.
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a fuzzy Linguistic Model to evaluate the quality of library 2 0 functionalities
International Journal of Information Management, 2013Co-Authors: Ruben Heradio, Francisco Javier Cabrerizo, David Fernandezamoros, Manuel Herrera, Enrique HerreraviedmaAbstract:Abstract Libraries incessantly undergo change determined by evolving user needs. These are often induced by the emergence of previously unavailable tools. Web 2.0 represents an example of such a need-shifting technology, which has led to an embrace of new user interactivity services for many library websites, thus coined Library 2.0. This paradigm shift calls for new evaluation Models to include the implementation of Web 2.0 technologies. The aim of this paper is to present such a Model, and to evaluate the quality of Library 2.0 functionalities, measuring the quality of the 2.0 services offered through the websites based on user perception. We adopt fuzzy Linguistic Modeling to represent user perception, and apply aggregation operations to Linguistic labels in order to evaluate the quality of the new services. Furthermore, our Model subsumes the LibQUAL+ methodology, allowing for the identification of specific 2.0 functionalities in need of improvement and of those outstandingly satisfied by the system.
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a fuzzy Linguistic Model to evaluate the quality of web sites that store xml documents
International Journal of Approximate Reasoning, 2007Co-Authors: Enrique Herreraviedma, Eduardo Peis, Jose M Moralesdelcastillo, S Alonso, Karina AnayaAbstract:The development of tools to find quality information on the Web is currently a pressing need. The aim of this paper is to present an evaluation Model based on fuzzy computing with words to measure the information quality of Web sites that store XML (eXtensible Markup Language) documents. This Model evaluates the information quality of Web sites using only users' perceptions, and therefore it is user-centered. Fuzzy Linguistic techniques are involved in the quality evaluation process to create a user-friendly framework. This Model is composed of two main components, an evaluation scheme to analyze the information quality of Web sites and a computing method of quality ratings of Web sites. The evaluation scheme presents both technical criteria related to the Web site characteristics, and criteria related to the content of XML documents stored in the Web sites. The quality ratings represent the ability of Web sites to meet user requirements. Linguistic quality ratings are obtained by combining Linguistic evaluation judgements provided by Web visitors on the different evaluation criteria. The computing method is based on two operators for fuzzy computing with words, the LOWA (Linguistic Ordered Weighted Averaging) operator and the LWA (Linguistic Weighted Averaging) operator. The later allows to manage relative importance degrees among quality criteria in the evaluation process. This Model uses the power of XML Schema language to improve the representation of documents in the Web with semantic characteristics related to their quality and thus it is useful to search quality resources in XML format. Web site quality ratings could be used by Web retrieval systems to help users to find the highest quality XML resources for their information needs. Additionally, this Model could be helpful to Web developers to improve the quality of Web sites from a user's point of view.
Deniz Uztürk - One of the best experts on this subject based on the ideXlab platform.
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Combined QFD TOPSIS approach with 2-tuple Linguistic information for warehouse selection
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017Co-Authors: Gülçin Büyüközkan, Deniz UztürkAbstract:2-tuple fuzzy Linguistic Model can be applied to eliminate vagueness/uncertainty in information. It also helps to deal with non-homogeneous information that occurs during group decision making (GDM) processes. GDM is generally applied to diminish the bias during the decision phase and to reduce the subjectivity of the decision process. Thus, this paper proposes an integrated GDM technique based on 2-tuple Linguistic Model, quality function deployment (QFD) and the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. This proposed framework is then applied to a green warehouse selection problem. The originality of this paper comes from its combination of these two methodologies together for the first time in literature in this specific field. The application area indicates its suitability for the proposed methodology.