International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

In this paper, we propose an interactive method for multiobjective linear programming problems, in which fuzzy coefficients, random variable coefficients and fuzzy random variable coefficients are involved in the objective functions respectively. In the proposed method, it is assumed that the decision maker has a fuzzy goal for each objective function, and such a fuzzy goal can be quantified by eliciting the membership function. Through a possibility measure and a fractile optimization model, the original problem is transformed to the well-defined multiobjective programming problem. Then, a generalized Pareto optimal concept is defined, and an interactive algorithm is proposed to obtain a satisfactory solution from among a generalized Pareto optimal solution set.

In recent years, singular systems and fuzzy descriptor have attracted a lot of researchers' attention due to their wide practical applications for modeling complex phenomena. In this study, one approach proposed is the fuzzy clustering algorithm based on linear structures to identify the neuro Fuzzy local linear models. Additionally fuzzy descriptor models, a recently proposed neuro fuzzy interpretation of locally linear models, are implemented because of their promise for intuitive incremental learning algorithms e.g. Generalized Fuzzy Clustering Variety (GFCV). The results from the fuzzy descriptor models are compared to the results of several other methods. An efficient technique, based on the error indices of multiple validation sets, is used to optimize the number of neurons and prevent the algorithm from over fitting. The scope of this work is to reveal the advantages of fuzzy descriptor models and compare them to the most successful neural and neuro fuzzy approaches based on prediction accuracy, generalization, and computational complexity. The proposed solution is shown to accurately forecast seismic time series, outperforming several other methods.

The recommender systems community is paying great attention to diversity as key qualities beyond accuracy in real recommendation scenarios. Multifarious diversity-increasing approaches have been developed to enhance recommendation diversity in the related literature while making personalized recommendations to users. In this work, we present Gaussian Cloud Recommendation Algorithm (GCRA), a novel method designed to balance accuracy and diversity personalized top-N recommendation lists in order to capture the user's complete spectrum of tastes. Our proposed algorithm does not require semantic information. Meanwhile we propose a unified framework to extend the traditional CF algorithms via utilizing GCRA for improving the recommendation system performance. Our work builds upon prior research on recommender systems. Though being detrimental to average accuracy, we show that our method can capture the user's complete spectrum of interests. Systematic experiments on three real-world data sets have demonstrated the effectiveness of our proposed approach in learning both accuracy and diversity.

Fuzzy clustering is a widely used approach for data classification by using the fuzzy set theory. The probability measure and the possibility measure are two popular measures which have been used in the fuzzy $c$-means algorithm (FCM) and the possibilistic clustering algorithms (PCAs), respectively. However, the numerical experiments revealed that FCM and its derivatives lack the intuitive concept of degree of belongingness, and PCAs suffer from the “coincident problem” and cannot provide very stable results for some data sets. In this study, we propose a new clustering algorithm, called the credibilistic clustering algorithm (CCA), based on the credibility measure. The credibility measure provides some unique properties which can solve the “coincident problem” and noise issue compared with the probability measure and possibility measure. Based on some randomly generated data sets, experimental results compared with FCM and PCA show that CCA can deal with the “coincident problem” with good clustering results, and it is more robust to noise than PCA.

In this paper, a new approach is presented for solving multi-criteria group decision-making (MCGDM) problems, which is based on new arithmetic operations and the ranking rules of trapezoidal interval type-2 fuzzy numbers (IT2FNs). Firstly, the shortcomings of some existing arithmetic operations of trapezoidal IT2FNs are discussed along with their ranking methods, before some new arithmetic operations and ranking rules are proposed. Secondly, some new aggregation operators including the arithmetic averaging aggregation operator, the ordered weighted averaging aggregation operator and the hybrid weighted averaging aggregation operator for trapezoidal IT2FNs are also developed. Thirdly, a new approach for MCGDM problems is developed based on the proposed operators and ranking rules. Finally, an example is provided to illustrate the feasibility and validity of this new approach, and a comparison analysis referring to the same example is also presented.

In this paper we discuss an important integrity constraint called multivalued dependency (mvd), which occurs as a result of the first normal form, in the framework of a newly proposed model called fuzzy multivalued relational data model. The fuzzy multivalued relational data model proposed in this paper accommodates a wider class of ambiguities by representing the domain of attributes as a “set of fuzzy subsets”. We show that our model is able to represent multiple types of impreciseness occurring in the real world. To compute the equality of two fuzzy sets/values (which occur as tuple-values), we use the concept of fuzzy functions. So the main objective of this paper is to extend the mvds in context of fuzzy multivalued relational model so that a wider class of impreciseness can be captured. Since the mvds may not exist in isolation, a complete axiomatization for a set of fuzzy functional dependencies (ffds) and mvds in fuzzy multivalued relational schema is provided and the role of fmvds in obtaining the lossless join decomposition is discussed. We also provide a set of sound Inference Rules for the fmvds and derive the conditions for these Inference Rules to be complete. We also derive the conditions for obtaining the lossless join decomposition of a fuzzy multivalued relational schema in the presence of the fmvds. Finally we extend the ABU's Algorithm to find the lossless join decomposition in context of fuzzy multivalued relational databases. We apply all of the concepts of fmvds developed by us to a real world application of “Technical Institute” and demonstrate that how the concepts fit well to capture the multiple types of impreciseness.

With respect to multiple attribute group decision making (MAGDM) problems, in which the attribute weights take the form of real numbers, and the attribute values take the form of intuitionistic fuzzy linguistic variables, a decision analysis approach is proposed. In this paper, we develop an intuitionistic fuzzy linguistic induce OWA (IFLIOWA) operator and analyze the properties of it by utilizing some operational laws of intuitionistic fuzzy linguistic variables. A new method based on the IFLIOWA operator for multiple attribute group decision making (MAGDM) is presented. Finally, a numerical example is used to illustrate the applicability and effectiveness of the proposed method.