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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]