Quantum-Behaved Particle Swarm Optimization Based Radial Basis Function Network for Classification of Clinical Datasets

N. Leema, H. Khanna Nehemiah and A. Kannan
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N. Leema: College of Engineering Guindy, Anna University, Chennai, India
H. Khanna Nehemiah: College of Engineering Guindy, Anna University, Chennai, India
A. Kannan: College of Engineering Guindy, Anna University, Chennai, India

International Journal of Operations Research and Information Systems (IJORIS), 2018, vol. 9, issue 2, 32-52

Abstract: In this article, a classification framework that uses quantum-behaved particle swarm optimization neural network (QPSONN) classifiers for diagnosing a disease is discussed. The neural network used for classification is radial basis function neural network (RBFNN). For training the RBFNN K-means clustering algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm has been used. The K-means clustering algorithm is used to find the optimal number of clusters which determines the number of neurons in the hidden layer. The cluster approximation error is used to find the optimal clusters. The weights between the hidden and the output layer is determined using QPSO algorithm based on the mean squared error (MSE). The performance of the developed classifier model has been tested with five clinical datasets, namely Pima Indian Diabetes, Hepatitis, Bupa Liver Disease, Wisconsin Breast Cancer and Cleveland Heart Disease were obtained from the University of California, Irvine (UCI) machine learning repository.

Date: 2018
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