International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

Since the International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems published its first issue in 1993, it has made important contributions to the research field of computer science. In this study, based on the dataset of the publications published in this journal between 1993 and 2016 retrieved from Web of Science, a general overview of this journal is performed using bibliometric methods and visualized networks. First, the productive and influential publications, authors, institutions, countries/territories, and supraregions are analysed based on the total number of citations, publications, and different citation thresholds. Second, network visualization analysis is applied to illustrate the links and connections between terms by using the VOSviewer software. Moreover, the most cited journals and common author keywords of three continents, including North America, Europe, and Asia, are also presented. This paper will hopefully help researchers understand the research patterns of this journal.

Linguistic summarization provides to express large volumes of quantitative data in easy understandable natural language based forms. While various methods have been recommended for linguistic summarization, there is not any approach for linguistic summarization where possibilistic and probabilistic uncertainties exist together in data. In this study, we establish a tie between Z-number concept and type-I and type-II quantified sentences in order to calculate the truth degree of a linguistic summary covering possibilistic and probabilistic information. The proposed approach employs copulas to obtain a joint probability distribution of the variables included in type-II quantified sentence.

Extensionality is explored form different points of view. Extensional fuzzy subsets from a fuzzy equivalence relation E are considered as observable subsets with respect to the granularity generated by E. Interestingly, they are characterized as the fuzzy subsets that can be obtained as combinations of the fuzzy equivalence classes of E. Extensional mappings are characterized topologically and the set of extensional mappings between two universes are algebraically determined. Specifying the results to fuzzy mappings from a universe X onto [0, 1] an interpretation of type-2 fuzzy subsets of X as fuzzification of its type-1 fuzzy subsets is provided.

This paper presents a group decision-making model using a distance aggregator based on Ordered Weighted Distance (OWD) which offers a solution that can reduce disagreement between decision makers (DMs). This paper discusses decision rules and sets out measures to evaluate compensatory effects that have a bearing on DMs’ opinions. The model uses formulations of distances to reveal the differences in opinion among DMs and discusses the meanings of distance and the information presented by each DM. Finally, a case study of a logistics problem is used to illustrate how the model is applied.

In probability theory, mathematical expectation of a random variable is very important. Choquet expectation (integral), as a generalization of mathematical expectation, is a powerful tool in various areas, mainly in generalized probability theory and decision theory. In vector spaces, combining Choquet expectation and Pettis integral has led to a challenging and an interesting subject for researchers. In this paper, we indicate and discuss a failure in the previous definition of Choquet-Pettis integral of Banach space-valued functions. To obtain a correct definition of Choquet-Pettis integral, an open problem concerning the linearity of the Choquet integral is stated.

Troiano and Díaz presented the analytic solution to the iterative ordered weights averaging (ItOWA) operator weights. Their findings validate Dujmović and Larsen’s conjecture and profoundly contribute to the ItOWA operator due to the closed formula derived. This paper attempts to present another way of deriving the ItOWA operator weights based on other theories. Further, as iterative OWA performs repeated computations for a higher dimension, the orness of the resulting ItOWA operator weights is different from the one initially used in aggregating two input arguments. To resolve this unwanted situation, we suggest some weighting functions that generate the OWA operator weights having a property of constant orness irrespective of the number of input arguments.

In trust management systems, the trustor should be able to select a trustee candidate that has the maximum trustworthiness degree toward a specific goal and an amount of risk consistent with her/his risk acceptance degree. In this research, a novel computational trust model based on the principles of uncertainty theory is introduced. In the proposed model, trust is considered to be constructed of trustworthiness components. To calculate each of these trustworthiness components, empirical distributions of recommenders and trustor’s opinions about the existing trustworthiness and risk degrees of the trustee candidates are aggregated. In the decision making stage, the trustee candidate with the optimum trustworthiness and risk degrees is selected according to uncertain goal programming. Based on this method, trustworthiness and risk degrees of the trustee candidates are calculated according to the amount of negative and positive deviations from the optimal state. To verify the accuracy of the model’s behavior, a series of simulation scenarios are constructed. The results of these simulations demonstrate that the proposed model effectively selects the best trustee candidate according to parameters such as context, priorities of the trustworthiness components, trustor’s constraints and the trustworthiness and risk acceptance degrees. Finally, by comparing the model with other commonly used computational trust modeling approaches, it is shown that the proposed model has a lower mean absolute error (MAE) and produces more accurate results.

The evaluation models for long time historical data is important in many applications. In this study, based on Age measure defined by Yager, we propose the definitions of Age Sequence and Age Series. Then, we provide a Generalized Recursive Smoothing method. Some classical smoothing models in evaluation problems can be seen as special cases of Generalized Recursive Smoothing method. In order to obtain more reasonable and effective aggregation results of the historical data, we propose some different Age Sequences, e.g., the Generalized Harmonic Age Sequence and p Age Sequence, which theoretically can provide infinite more recursive smoothing methods satisfying different preferences of decision makers.

This paper proposes an uncertain multi-period bi-level network interdiction problem with uncertain arc capacities. It is proved that there exists an equivalence relationship between uncertain multi-period network interdiction problem and the obtained deterministic correspondent. Application of the generalized Benders’ decomposition algorithm is considered as the solution approach to the resulting mixed-integer nonlinear programming problem. Finally, a numerical example is presented to illustrate the model and the algorithm.

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA.