Reinforced Fuzzy-Rule-Based Neural Networks Realized Through Streamlined Feature Selection Strategy and Fuzzy Clustering With Distance Variation

In this article, we present a dimensionality reduction methodology of reinforced fuzzy-rule-based neural networks (FRNNs) realized with the help of determination/correlation coefficient-based streamlined feature selection strategy and fuzzy clustering with standard deviation to cope with high-dimensional data. This approach aims to reduce the design process of the proposed networks and to curb the computational overhead inherently associated with the increasing volume of data both in terms of their number and the dimensionality of the feature space. The overall architecture and learning mechanism of the FRNNs are based on radial basis function neural networks. However, we design the hidden layer of RBFNNs differently by using fuzzy clustering, which makes it easy to determine the parameters, such as centers and widths of the receptive fields (activation functions). Unlike conventional neural networks, the RBFNNs do not have a feature to support dimensionality reduction. To overcome this limitation, FRNNs select input variables by evaluating the adjusted determination coefficient of the model. To reduce the computational burden of finding an appropriate combination of inputs, we propose a simplified feature selection and elimination technique, in which the variables are selected or eliminated by correlation coefficients. A linear function expresses the connection weight, and we apply L2-norm regularization to least-square-error-based learning to estimate stable coefficients (weights), which is expected to significantly improve the generalization ability. The superiority of the proposed FRNNs was demonstrated by using 28 real-world benchmark datasets. The networks are also compared with the conventional models associated with the FRNNs and several related models previously published in the literature.