PSO-RBFNN: A PSO-based clustering approach for RBFNN design to classify disease data

Abstract

The Radial Basis Function Neural Networks (RBFNNs) are non-iterative in nature so they are attractive for disease classification. These are four layer networks with input, hidden, output and decision layers. The RBFNNs require single iteration for training the network. On the other side, it suffers from growing hidden layer size on par with training dataset. Though various attempts have been made to solve this issue by clustering the input data. But, in a given dataset estimating the optimal number of clusters is unknown and also it involves more computational time. Hence, to address this problem in this paper, a Particle Swarm Optimization (PSO)-based clustering methodology has been proposed. In this context, we introduce a measure in the objective function of PSO, which allows us to measure the quality of wide range of clusters without prior information. Next, this PSO-based clustering methodology yields a set of High-Performance Cluster Centers (HPCCs). The proposed method experimented on three medical datasets. The experimental results indicate that the proposed method outperforms the competing approaches.