Radial Basis Function (RBF) Neural Network Classification based on Consistency Evaluation Measure

Abstract

researchers have been applied artificial neural networks in clinical diagnosis, image analysis, signal analysis, interpretation and various classification problems. Among artificial neural networks, RBF neural network has a single hidden layer and it is used to classify complex problems, whereas an MLP may have one or more hidden layers. Many feature selection methods have become important preprocessing steps to improve training performance and accuracy before classification. Consistency-based feature selection is an important category of feature selection research. This paper presents about RBF neural network classification based on consistency measure for medical datasets. There are irrelevant features in medical dataset and it becomes easier to train RBF network by removing unnecessary features. Therefore, this paper shows higher accuracy, better network performance and less time complexity by using RBF classifier based on consistency based feature selection.