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Abstract Fuzzy rule-based classifiers have been used in many applications showing better performance due to simple modular architectures and significant interpretability. Generally, researchers design this type of classifiers along with two incompatible directions: some of them devote to mine more rules from data for enhancing the accuracy of classifiers and others focus on reducing the number of rules for simplifying the classifier with high accuracy. However, from the perspective of practicability, the tradeoff between the accuracy of model and the number of rules including in the model should be carefully considered when designing classifiers. Further, these classifiers also encounter two evident limitations in the process of design: one is that the contribution of each sample versus individual attributions is equally important and another is that the concept of information granularity is not considered. These two limitations result in the reduction of accuracy of the ensuing classifier. To alleviate these two limitations and make compromise between the accuracy and the number of rules of formed classifier, in this study, a novel method is proposed to construct a fuzzy classifier by means of the principle of justifiable granularity with weighted data. The proposed method involves two main stages: the first stage concerns with the formation of an initial classification model. The initial model is constructed by engaging a synergy of Fuzzy C-Means clustering (FCM) and the principle of justifiable granularity with weighted data. The second stage focuses on the refinement of the initial classification model. The number of rules of the completely formed fuzzy classification model is equal to that of classes of experimental data. A series of experiments concerning synthetic datasets and ten UCI datasets are also implemented to exhibit the feasibility and effectiveness of the proposed classification method as well as reveal the impact of information granularity on resulting classification model. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we introduce a new category of fuzzy models called a fuzzy ensemble of parallel polynomial neural network (FEP2N2), which consist of a series of polynomial neural networks weighted by activation levels of information granules formed with the use of fuzzy clustering. The two underlying design mechanisms of the proposed networks rely on information granules resulting from the use of fuzzy C-means clustering (FCM) and take advantage of polynomial neural networks (PNNs). The resulting model comes in the form of parallel polynomial neural networks. In the design procedure, in order to estimate the optimal values of the coefficients of polynomial neural networks we use a weighted least square estimation algorithm. We incorporate various types of structures as the consequent part of the fuzzy model when using the learning algorithm. Among the diverse structures being available, we consider polynomial neural networks, which exhibit highly nonlinear characteristics when being viewed as local learning models. We use FCM to form information granules and to overcome the high dimensionality problem. We adopt PNNs to find the optimal local models, which can describe the relationship between the input variables and output variable within some local region of the input space. We show that the generalization capabilities as well as the approximation abilities of the proposed model are improved as a result of using polynomial neural networks. The performance of the network is quantified through experimentation in which we use a number of benchmarks already exploited within the realm of fuzzy or neurofuzzy modeling. [Copyright &y& Elsevier]

The use of the hesitant fuzzy linguistic term sets (HFLTSs) has recently become an important trend in fuzzy decision making, and aggregating HFLTSs and their extensions has now become crucial for making decisions. Previous approaches to aggregating possibility distributions for HFLTSs were based on the paradigm of computing with words, whereas few proposals have been made to aggregate HFLTS possibility distributions under the framework of statistical data analysis so as to reduce information loss and distortion. An initial attempt was the similarity-measure-based agglomerative hierarchical clustering (SM-AggHC) two-stage aggregation paradigm for HFLTS possibility distributions, which, however, presents some important performance limitations from time complexity and memory requirement perspectives. Thereby, this paper introduces a new approach, so called, " N -two-stage algorithmic aggregation paradigm driven by the K -means clustering" (N2S-KMC) to overcome these limitations by cardinality reduction in the first stage of the aggregation process. The subsequent stage uses the similarity-measure-based K -means clustering algorithm to outperform the SM-AggHC algorithm. Such an outperformance, from run time and memory usage, is demonstrated by experimental results. [ABSTRACT FROM AUTHOR]

In group decision-making (GDM), a consensus building process (CRP) is necessary to improve the level of consensus within the group. This paper develops a new CRP model for GDM problems with hesitant fuzzy linguistic term sets (HFLTSs), which also considers the reliability of experts. Firstly, we present the calculation of expert reliability by measuring the hesitancy degree and similarity degree, simultaneously. Next, we propose an approach to generate group opinions based on the reliability of experts within the framework of the Dempster–Shafer evidence theory (DSET). In this way, the original individual opinions can be retained well and the discourse power of experts with higher reliability but lower weights can be strengthened. Further, the reliability is introduced to build a new feedback mechanism that provides experts with explicit advice during the consensus reaching process. Subsequently, a new CRP algorithm for hesitant fuzzy linguistic GDM (HFLGDM) problems is proposed to help the group achieve consensus. A practical case study is utilized to illustrate the application of the proposed algorithm. Finally, we conduct a comparison experiment to verify the advantages of the proposed CRP algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we study on how to achieve balance between minimizing the objectives, satisfying the constraints, and avoiding the population to stuck at locally optimal or locally feasible regions. We propose a new constraints handling technique based on diversity distance measure for constrained multiobjective optimization problems. We innovatively measure all effects of the constraints on the objectives by the interaction effects and direct effects of constraints on the objectives. The interactive effects of constraints on the objective functions are firstly expressed and quantified by using Mahalanobis distance and computing the value of the distance measure by involving a Lebesgue measure. Based on interactive and direct effects, we propose a new modified mechanism of objective space, called objective space modified mechanism based on diversity distance measure, so that the good infeasible solution could be more effectively used to find the optimal solution. The Pull and Push search is used to adaptively adjust the location of the Pareto front, which could preclude the population from being stuck at some locally optimal or locally feasible regions and could decrease time complexity. Comprehensive experiments completed for several benchmark problems demonstrate the competitiveness of the proposed algorithm, in comparison to the existing state-of-art constrained evolutionary multiobjective optimization. [ABSTRACT FROM AUTHOR]

The mobile wireless networks (MWNs) are flexible and scalable. They have been widely used in large-scale monitoring, search and rescue tasks, pursuit and escape operations and other scenarios. However, due to the high cost of wireless sensors in MWNs, the cost of increasing wireless sensor nodes is relatively high, so it is generally monitored by the limited nodes moving in the monitoring range. Having this in mind, we design an original algorithm to improve the monitoring efficiency of mobile wireless nodes, named Distributed Guidance Anti-flocking Algorithm (DGAA). Because most of the dynamic coverage algorithms are difficult to adapt to the complex operating environment. Furthermore the high overlapping rate of the wireless nodes is easy to cause resource waste. In light of these factors, the DGAA has a low overlapping rate of mobile wireless nodes in the complex environment with obstacles. DGAA mimics the predatory behavior of solitary animals. These animals can go out on their own to catch prey, and they do not leave their homes. At the same time, they can prevent the invasion of foreign individuals and ensure their maximum profits. The experiment is simulated on a map of 50m*50m, and the results are compared with those produced by competitive algorithms. Experimental results show that DGAA exhibits better performance than other algorithms. [ABSTRACT FROM AUTHOR]