Design methodology for Radial Basis Function Neural Networks classifier based on locally linear reconstruction and Conditional Fuzzy C-Means clustering

In this study, a new design method for Fuzzy Radial Basis Function Neural Networks classifier is proposed. The proposed approach is based on conditional Fuzzy C-Means clustering algorithm realized with the aid of auxiliary information, which is extracted by the locally linear reconstruction algorithm. Conditional Fuzzy C-Means can analyze the distribution of data (patterns) over the input space when being supervised by the auxiliary information. The conditional fuzzy C-Means clustering can substitute the conventional fuzzy C-Means clustering which has been usually used to define the radial basis functions over the input space. It is advocated that the auxiliary information extracted by using the locally linear reconstruction can determine which patterns among the entire data set are more important than the others. This assumption is based on the observation that the data, which cannot be fully reconstructed by the linear combination of their neighbors, may convey much more information than the other data to be reconstructed. It is well known that in the case of radial basis function neural networks classifier, the classification performance of this classifier is predominantly based on the distribution of the radial basis function over the input space. Several experiments are provided to verify the proposed design method for classification problems. (C) 2019 Elsevier Inc. All rights reserved.