In group decision making, one strives to reconcile differences of opinions (judgments) expressed by individual members of the group. Fuzzy-decision-making mechanisms bring a great deal of flexibility. By admitting membership degrees, we are offered flexibility to exploit different aggregation mechanisms and navigate a process of interaction among decision makers to achieve an increasing level of consistency within the group. While the studies reported so far exploit more or less sophisticated ways of adjusting/transforming initial judgments (preferences) of individuals, in this paper, we bring forward a concept of information granularity. Here, information granularity is viewed as an essential asset, which offers a decision maker a tangible level of flexibility using some initial preferences conveyed by each individual that can be adjusted with the intent to reach a higher level of consensus. Our study is concerned with an extension of the well-known analytic hierarchy process to the group decision-making scenario. More specifically, the admitted level of granularity gives rise to a granular matrix of pairwise comparisons. The granular entries represented, e.g., by intervals or fuzzy sets, supply a required flexibility using the fact that we select the most suitable numeric representative of the reciprocal matrix. The proposed concept of granular reciprocal matrices is used to optimize a performance index, which comes as an additive combination of two components. The first one expresses a level of consistency of the individual pairwise comparison matrices; by exploiting the admitted level of granularity, we aim at the minimization of the corresponding inconsistency index. The second part of the performance index quantifies a level of disagreement in terms of the individual preferences. The flexibility offered by the level of granularity is used to increase the level of consensus within the group. Given an implicit nature of relationships between the realizations of the granular pairwise matrices and the values of the performance index, we consider using particle swarm optimization as an optimization vehicle. Two scenarios of allocation of granularity among decision makers are considered, namely, a uniform allocation of granularity and nonuniform distribution of granularity, where the levels of allocated granularity are also subject to optimization. A number of numeric studies are provided to illustrate an essence of the method.
Semantic Web is an important step towards significant changes in representation and utilization of data on the web. The use of Resource Description Framework (RDF) as a fundamental data format creates new ways of expressing and exploring relations existing between pieces of data. An importance of articulating temporal aspects using RDF data, and a need to query these data using vague and imprecise terms is a challenging undertaking. This paper presents a fuzzy-based approach to deal with RDF data containing temporal information and built using non-trivial data structures (RDF Schemas). It includes description of built-in predicates needed for constructing temporal queries and supporting imprecise phrases describing time and data features. A simple case study using DBLP database illustrates the application of the proposed approach.
Linguistic interface is a group of linguistic terms or fuzzy descriptions that describe variables in a system utilizing corresponding membership functions. Its transparency completely or partly decides the interpretability of fuzzy models. This paper proposes a GRadiEnt-descEnt-based Transparent lInguistic iNterface Generation (GREETING) approach to overcome the disadvantage of traditional linguistic interface generation methods where the consideration of the interpretability aspects of linguistic interface is limited. In GREETING, the widely used interpretability criteria of linguistic interface are considered and optimized. The numeric experiments on the data sets from University of California, Irvine (UCI) machine learning databases demonstrate the feasibility and superiority of the proposed GREETING method. The GREETING method is also applied to fuzzy decision tree generation. It is shown that GREETING generates better transparent fuzzy decision trees in terms of better classification rates and comparable tree sizes.
The results indicated that circulating microRNAs can be used as a non-invasive biomarker for the diagnosis of melanoma.
The objective of this paper is to study facility location problems under a hybrid uncertain environment involving randomness and fuzziness. A two-stage fuzzy random facility location model with recourse is developed in which the demands and the costs are assumed to be fuzzy random variables. As in general the fuzzy random parameters in the model can be regarded as continuous fuzzy random variables with infinite realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this fact, the recourse function cannot be calculated analytically, which implies that the model cannot benefit from the use of methods of classical mathematical programming. In order to solve the location problems of this nature, we first develop techniques of fuzzy random simulation. In the sequel, by combining the fuzzy random simulation, simplex algorithm and binary particle swarm optimization (BPSO), a hybrid algorithm is proposed to solve the two-stage fuzzy random facility location model. Finally, an illustrative numerical example is provided.
