The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Francisco Herrera - One of the best experts on this subject based on the ideXlab platform.
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connecting the Linguistic hierarchy and the numerical scale for the 2 tuple Linguistic model and its use to deal with hesitant unbalanced Linguistic information
Information Sciences, 2016Co-Authors: Yucheng Dong, Congcong Li, Francisco HerreraAbstract:The 2-tuple Linguistic Representation model is widely used as a basis for computing with words (CW) in Linguistic decision making problems. Two different models based on Linguistic 2-tuples (i.e., the model of the use of a Linguistic hierarchy and the numerical scale model) have been developed to address term sets that are not uniformly and symmetrically distributed, i.e., unbalanced Linguistic term sets (ULTSs). In this study, we provide a connection between these two different models and prove the equivalence of the Linguistic computational models to handle ULTSs. Further, we propose a novel CW methodology where the hesitant fuzzy Linguistic term sets (HFLTSs) can be constructed based on ULTSs using a numerical scale. In the proposed CW methodology, we present several novel possibility degree formulas for comparing HFLTSs, and define novel operators based on the mixed 0-1 linear programming model to aggregate the hesitant unbalanced Linguistic information.
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a web tool to support decision making in the housing market using hesitant fuzzy Linguistic term sets
Applied Soft Computing, 2015Co-Authors: Rosana Montes, Ana M Sanchez, Pedro Villar, Francisco HerreraAbstract:Graphical abstractDisplay Omitted In this paper we present a Linguistic multiple-expert multi-criteria decision making model and a web tool to support it, that is centred on the housing market. The web tool is integrated with the usual catalogue of resources for rental or for sale, enriched with the possibility of ranking a subset of properties according to the client's preferences and the internal knowledge associated to the properties. Usually the description of a property is quantitative, thought in our case we add qualitative information corresponding to assessments made by housing agents. These agents are considered experts in the market conditions.We apply the 2-tuple Linguistic Representation model to keep accuracy in the processes of Computing with Words and the hesitant fuzzy Linguistic term sets to qualify in situations of uncertainty and hesitation in the assessments. The software helps the agents in the process of the elicitation of the Linguistic expression based on the fuzzy Linguistic approach and the use of context-free grammars, and the web clients in the decision of visiting a property.
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Linguistic approaches based on the 2 tuple fuzzy Linguistic Representation model
2015Co-Authors: Luis Martinez, Rosa M Rodriguez, Francisco HerreraAbstract:Linguistic modelling has been applied to decision making, among other research fields, since the beginning of the 1980s with successful and interesting results. The introduction of the 2-tuple Linguistic model opened the door to a further intensive, extensive, and deeper study of the use of Linguistic information and Computing with Words by using symbolic approaches in different applications, mainly in the decision-making field and related topics. Such a study has attracted the attention of many scientists whose research concerns how to improve the use of symbolic models for Computing with Words in Linguistic decision making. As a result of such research some new symbolic approaches have been developed that try to improve different aspects of the 2-tuple Linguistic model; several of these approaches are directly based on it and aim at overcoming some specific limitations of the 2-tuple Linguistic model. This chapter presents a review of several of those symbolic approaches that are based on it and its concepts.
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a genetic tuning to improve the performance of fuzzy rule based classification systems with interval valued fuzzy sets degree of ignorance and lateral position
International Journal of Approximate Reasoning, 2011Co-Authors: Jose Antonio Sanz, Alberto Fernandez, Humberto Bustince, Francisco HerreraAbstract:Fuzzy Rule-Based Systems are appropriate tools to deal with classification problems due to their good properties. However, they can suffer a lack of system accuracy as a result of the uncertainty inherent in the definition of the membership functions and the limitation of the homogeneous distribution of the Linguistic labels. The aim of the paper is to improve the performance of Fuzzy Rule-Based Classification Systems by means of the Theory of Interval-Valued Fuzzy Sets and a post-processing genetic tuning step. In order to build the Interval-Valued Fuzzy Sets we define a new function called weak ignorance for modeling the uncertainty associated with the definition of the membership functions. Next, we adapt the fuzzy partitions to the problem in an optimal way through a cooperative evolutionary tuning in which we handle both the degree of ignorance and the lateral position (based on the 2-tuples fuzzy Linguistic Representation) of the Linguistic labels. The experimental study is carried out over a large collection of data-sets and it is supported by a statistical analysis. Our results show empirically that the use of our methodology outperforms the initial Fuzzy Rule-Based Classification System. The application of our cooperative tuning enhances the results provided by the use of the isolated tuning approaches and also improves the behavior of the genetic tuning based on the 3-tuples fuzzy Linguistic Representation.
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learning the membership function contexts for mining fuzzy association rules by using genetic algorithms
Fuzzy Sets and Systems, 2009Co-Authors: Jesus Alcalafdez, Rafael Alcala, Maria Jose Gacto, Francisco HerreraAbstract:Different studies have proposed methods for mining fuzzy association rules from quantitative data, where the membership functions were assumed to be known in advance. However, it is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for mining fuzzy association rules. This paper thus presents a new fuzzy data-mining algorithm for extracting both fuzzy association rules and membership functions by means of a genetic learning of the membership functions and a basic method for mining fuzzy association rules. It is based on the 2-tuples Linguistic Representation model allowing us to adjust the context associated to the Linguistic term membership functions. Experimental results show the effectiveness of the framework.
Yucheng Dong - One of the best experts on this subject based on the ideXlab platform.
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connecting the Linguistic hierarchy and the numerical scale for the 2 tuple Linguistic model and its use to deal with hesitant unbalanced Linguistic information
Information Sciences, 2016Co-Authors: Yucheng Dong, Congcong Li, Francisco HerreraAbstract:The 2-tuple Linguistic Representation model is widely used as a basis for computing with words (CW) in Linguistic decision making problems. Two different models based on Linguistic 2-tuples (i.e., the model of the use of a Linguistic hierarchy and the numerical scale model) have been developed to address term sets that are not uniformly and symmetrically distributed, i.e., unbalanced Linguistic term sets (ULTSs). In this study, we provide a connection between these two different models and prove the equivalence of the Linguistic computational models to handle ULTSs. Further, we propose a novel CW methodology where the hesitant fuzzy Linguistic term sets (HFLTSs) can be constructed based on ULTSs using a numerical scale. In the proposed CW methodology, we present several novel possibility degree formulas for comparing HFLTSs, and define novel operators based on the mixed 0-1 linear programming model to aggregate the hesitant unbalanced Linguistic information.
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consensus based group decision making under multi granular unbalanced 2 tuple Linguistic preference relations
Group Decision and Negotiation, 2015Co-Authors: Yucheng DongAbstract:In group decision making (GDM) situations, it is quite natural that the decision makers who may have different background and knowledge will provide their preferences by means of different Linguistic term sets. Specifically, multi-granular Linguistic term sets that are not uniformly and symmetrically distributed will be employed. To deal with this type of GDM problems, this paper proposes a consensus-based GDM model by using two existing 2-tuple Linguistic Representation models (i.e., the Herrera and Martinez model and the Wang and Hao model), which we called the GDM model based on multi-granular unbalanced 2-tuple Linguistic preference relations. First, the framework of the GDM model with multi-granular unbalanced 2-tuple Linguistic preference relations is proposed. Then, the transformation function is obtained to relate multi-granular unbalanced Linguistic preference relations with uniform balanced Linguistic preference relations. Further, a consensus model is presented to help the decision makers reach a consensus. This consensus model not only provides a new way to simultaneously manage individual consistency and group consensus in a linear programming model, but also minimizes information loss (or consensus cost) when reaching the established consensus level. Finally, an example is given to illustrate the feasibility and validity of the proposed model.
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the owa based consensus operator under Linguistic Representation models using position indexes
European Journal of Operational Research, 2010Co-Authors: Yucheng Dong, Bo FengAbstract:When using Linguistic approaches to solve decision problems, we need Linguistic Representation models. The symbolic model, the 2-tuple fuzzy Linguistic Representation model and the continuous Linguistic model are three existing Linguistic Representation models based on position indexes. Together with these three Linguistic models, the corresponding ordered weighted averaging operators, such as the Linguistic ordered weighted averaging operator, the 2-tuple ordered weighted averaging operator and the extended ordered weighted averaging operator, have been developed, respectively. In this paper, we analyze the internal relationship among these operators, and propose a consensus operator under the continuous Linguistic model (or the 2-tuple fuzzy Linguistic Representation model). The proposed consensus operator is based on the use of the ordered weighted averaging operator and the deviation measures. Some desired properties of the consensus operator are also presented. In particular, the consensus operator provides an alternative consensus model for group decision making. This consensus model preserves the original preference information given by the decision makers as much as possible, and supports consensus process automatically, without moderator.
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computing the numerical scale of the Linguistic term set for the 2 tuple fuzzy Linguistic Representation model
IEEE Transactions on Fuzzy Systems, 2009Co-Authors: Yucheng Dong, Yinfeng Xu, Shui YuAbstract:When using Linguistic approaches to solve decision problems, we need the techniques for computing with words (CW). Together with the 2-tuple fuzzy Linguistic Representation models (i.e., the Herrera and Martinez model and the Wang and Hao model), some computational techniques for CW are also developed. In this paper, we define the concept of numerical scale and extend the 2-tuple fuzzy Linguistic Representation models under the numerical scale. We find that the key of computational techniques based on Linguistic 2-tuples is to set suitable numerical scale with the purpose of making transformations between Linguistic 2-tuples and numerical values. By defining the concept of the transitive calibration matrix and its consistent index, this paper develops an optimization model to compute the numerical scale of the Linguistic term set. The desired properties of the optimization model are also presented. Furthermore, we discuss how to construct the transitive calibration matrix for decision problems using Linguistic preference relations and analyze the linkage between the consistent index of the transitive calibration matrix and one of the Linguistic preference relations. The results in this paper are pretty helpful to complete the fuzzy 2-tuple Representation models for CW.
Samad Ahmadi - One of the best experts on this subject based on the ideXlab platform.
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web usage mining with evolutionary extraction of temporal fuzzy association rules
Knowledge Based Systems, 2013Co-Authors: Stephen G Matthews, Mario Gongora, Adrian A Hopgood, Samad AhmadiAbstract:In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the intersection of fuzzy sets' boundaries where there is less support (lower membership), so the rules are lost. A genetic algorithm (GA)-based solution is described that uses the flexible nature of the 2-tuple Linguistic Representation to discover rules that occur at the intersection of fuzzy set boundaries. The GA-based approach is enhanced from previous work by including a graph Representation and an improved fitness function. A comparison of the GA-based approach with a traditional approach on real-world Web log data discovered rules that were lost with the traditional approach. The GA-based approach is recommended as complementary to existing algorithms, because it discovers extra rules.
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temporal fuzzy association rule mining with 2 tuple Linguistic Representation
IEEE International Conference on Fuzzy Systems, 2012Co-Authors: Stephen G Matthews, Mario Gongora, Adrian A Hopgood, Samad AhmadiAbstract:This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple Linguistic Representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of Linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules.
Enrique Herreraviedma - One of the best experts on this subject based on the ideXlab platform.
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sictqual a fuzzy Linguistic multi criteria model to assess the quality of service in the ict sector from the user perspective
Applied Soft Computing, 2015Co-Authors: Andŕes Cidlopez, Enrique Herreraviedma, Miguel J Hornos, Ram On Alberto CarrascoAbstract:Abstract The Information and Communication Technologies (ICTs) play an important role in the economic development, making it necessary to assess the quality of service perceived by consumers in this sector. The most effective quality assessment from the consumer perspective is still to be researched, yet the most common approach is oriented towards quantitative indicators. This study proposes to use a two-dimensional model that combines the widely accepted segmentation of ICTs with elements from the SERVQUAL quality model. This model, useful in multi-criteria decision-making situations, has been developed using the 2-tuple Linguistic Representation and fuzzy logic principles. This methodology prevents data loss during processing and provides relevant information through 16 indicators related to the quality of service. Besides, an expert-based mechanism is defined for the use of historical information extracted from completed surveys. As a practical case, this mechanism is applied to the historical information of a telecommunications company for assessing the quality of the service provided to its customers.
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a communication model based on the 2 tuple fuzzy Linguistic Representation for a distributed intelligent agent system on internet
Soft Computing, 2002Co-Authors: Miguel Delgado, Francisco Herrera, Enrique Herreraviedma, Maria J Martinbautista, Luis Martinez, M A VilaAbstract:Internet users are assisted by means of distributed intelligent agents in the information gathering process to find the fittest information to their needs. In this paper we present a distributed intelligent agent model where the communication of the evaluation of the retrieved information among the agents is carried out by using Linguistic operators based on the 2-tuple fuzzy Linguistic Representation as a way to endow the retrieval process with a higher flexibility, uniformity and precision. The 2-tuple fuzzy Linguistic Representation model allows to make processes of computing with words without loss of information.
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fusion of multigranular Linguistic information based on the 2 tuple fuzzy Linguistic Representation model
2002Co-Authors: Francisco Herrera, Enrique Herreraviedma, Luis Martinez, Francisco ChiclanaAbstract:The Fuzzy Linguistic Approach has been applied successfully to many problems, its use implies processes of Computing with Words (CW). One important limitation of the fuzzy Linguistic approach appears when these processes are applied to problems defined in multigranular Linguistic contexts. This limitation consists of the difficulty in dealing with this type of information in processes of CW, due to the fact, that there is no standard normalization process for this type of information as in the numerical domain. In this contribution, taking as base the 2-tuple fuzzy Linguistic Representation model and its computational technique, we shall present a method for easily dealing with multigranular Linguistic information in fusion processes.
Rafael Alcala - One of the best experts on this subject based on the ideXlab platform.
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comparison and design of interpretable Linguistic vs scatter frbss gm3m generalization and new rule meaning index for global assessment and local pseudo Linguistic Representation
Information Sciences, 2014Co-Authors: Marta Galende, Maria Jose Gacto, G I Sainz, Rafael AlcalaAbstract:This work is devoted to defining more general interpretability indexes to be applied to any scatter or Linguistic model implemented by any type of membership functions. They are based on metrics that should take into account the semantic and inference issues: the semantic issue in order to preserve the meaning of the Linguistic labels and the inference issue since this can influence the behavior of the rules. On the other hand, these metrics have been designed to be intuitive in order to support the analysis or selection of a final model and to favor a low computational cost within an optimization process. In order to check their usefulness, a multi-objective evolutionary algorithm, simultaneously performing a rule selection and an adjustment of the fuzzy partitions, is guided by the proposed indexes on several benchmark data sets to obtain models with different degrees of accuracy and interpretability. In addition, using these metrics, a local analysis can be carried out between models of a different nature. This local analysis through the model components, gives support to the user to make the best choice from among the models.
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learning the membership function contexts for mining fuzzy association rules by using genetic algorithms
Fuzzy Sets and Systems, 2009Co-Authors: Jesus Alcalafdez, Rafael Alcala, Maria Jose Gacto, Francisco HerreraAbstract:Different studies have proposed methods for mining fuzzy association rules from quantitative data, where the membership functions were assumed to be known in advance. However, it is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for mining fuzzy association rules. This paper thus presents a new fuzzy data-mining algorithm for extracting both fuzzy association rules and membership functions by means of a genetic learning of the membership functions and a basic method for mining fuzzy association rules. It is based on the 2-tuples Linguistic Representation model allowing us to adjust the context associated to the Linguistic term membership functions. Experimental results show the effectiveness of the framework.
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genetic learning of accurate and compact fuzzy rule based systems based on the 2 tuples Linguistic Representation
International Journal of Approximate Reasoning, 2007Co-Authors: Rafael Alcala, Francisco Herrera, Jesus Alcalafdez, Jose OteroAbstract:One of the problems that focus the research in the Linguistic fuzzy modeling area is the trade-off between interpretability and accuracy. To deal with this problem, different approaches can be found in the literature. Recently, a new Linguistic rule Representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the Linguistic 2-tuples Representation that allows the lateral displacement of a label considering an unique parameter. This way to work involves a reduction of the search space that eases the derivation of optimal models and therefore, improves the mentioned trade-off. Based on the 2-tuples rule Representation, this work proposes a new method to obtain Linguistic fuzzy systems by means of an evolutionary learning of the data base a priori (number of labels and lateral displacements) and a simple rule generation method to quickly learn the associated rule base. Since this rule generation method is run from each data base definition generated by the evolutionary algorithm, its selection is an important aspect. In this work, we also propose two new ad hoc data-driven rule generation methods, analyzing the influence of them and other rule generation methods in the proposed learning approach. The developed algorithms will be tested considering two different real-world problems.