On fuzzy-qualitative descriptions and entropy

This paper models the assessments of a group of experts when evaluating different magnitudes, features or objects by using linguistic descriptions. A new general representation of linguistic descriptions is provided by unifying ordinal and fuzzy perspectives. Fuzzy qualitative labels are proposed as a generalization of the concept of qualitative labels over a well-ordered set. A lattice structure is established in the set of fuzzy-qualitative labels to enable the introduction of fuzzy-qualitative descriptions as L-fuzzy sets. A theorem is given that characterizes finite fuzzy partitions using fuzzy-qualitative labels, the cores and supports of which are qualitative labels. This theorem leads to a mathematical justification for commonly-used fuzzy partitions of real intervals via trapezoidal fuzzy sets. The information of a fuzzy-qualitative label is defined using a measure of specificity, in order to introduce the entropy of fuzzy-qualitative descriptions. (C) 2016 Elsevier Inc. All rights reserved.Peer ReviewedPostprint (author's final draft

Using qualitative reasoning in modelling consensus in group decision-making

Ordinal scales are commonly used in rating and evaluation processes. These processes usually involve group decision making by means of an experts’ committee. In this paper a mathematical framework based on the qualitative model of the absolute orders of magnitude is considered. The entropy of a qualitatively described system is defined in this framework. On the one hand, this enables us to measure the amount of information provided by each evaluator and, on the other hand, the coherence of the evaluation committee. The new approach is capable of managing situations where the assessment given by experts involves different levels of precision. The use of the proposed measures within an automatic system for group decision making will contribute towards avoiding the potential subjectivity caused by conflicts of interests of the evaluators in the group.Postprint (published version

A recommender system based on group consensus

This paper presents the foundation for a new methodology for a collaborative recommender system (RS). This methodology is based on the degree of consensus of a group of users stating their preferences via qualitative orders-of-magnitude. The structure of distributive lattice is considered in defining the distance between users and the RSs new users. This proposed methodology incorporates incomplete or partial knowledge into the recommendation process using qualitative reasoning techniques to obtain consensus of its users for recommendations.Postprint (published version

L-Fuzzy sets for group linguistic preference modeling: an application to assess a firm’s performance

Given a finite totally ordered set of linguistic descriptions, the extended set of qualitative labels with different levels of precision L is constructed. In this framework, qualitative descriptions of a given set are L-fuzzy sets. A distance between L-fuzzy sets is introduced based on the properties of the lattice L. An illustrative example in the retail sector applied to assess a firm’s overall performance using perceptions of managers in the firm’s different departments is presented.Given a finite totally ordered set of linguistic descriptions, the extended set of qualitative labels with different levels of precision L is constructed. In this framework, quali- tative descriptions of a given set are L -fuzzy sets. A distance between L -fuzzy sets is introduced based on the properties of the lattice L . An illustrative example in the retail sector applied to assess a firm’s overall performance using perceptions of managers in the firm’s different departments is presented.Peer ReviewedPostprint (author’s final draft

Modeling group assessments by means of hesitant fuzzy linguistic term sets

Hesitant linguistic term sets have been introduced to capture the human way of reasoning using linguistic expressions involving different levels of precision. In this paper, a lattice structure is provided to the set of hesitant fuzzy linguistic term sets by means of the operations intersection and connected union. In addition, in a group decision making framework, hesitant fuzzy linguistic descriptions are defined to manage situations in which decision makers are assessing different alternatives by means of hesitant fuzzy linguistic term sets. Based on the introduced lattice structure, two distances between hesitant fuzzy linguistic descriptions are defined. These metric structures allow distances between decision makers to be computed. A centroid of the decision making group is proposed for each distance to model group representatives in the considered group decision making framework.Peer ReviewedPostprint (author's final draft

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