Generating Better Radial Basis Function Network for Large Data Set of Census

Radial basis function networks are known to have good performance compared to other artificial neural networks like multilayer perceptrons. Because the size of target data sets in data mining is very large and artificial neural networks including radial basis function networks require intensive computing, sampling is needed. So, because the sample size should be relatively small due to computational load to train radial basis function networks, simple random sampling for small size might not generate perfect and balanced samples. This paper suggests a better sampling technique based on branching information of decision tree for radial basis function networks when target data set is very large like census data. Experiments with census income data in UCI machine learning repository shows a promising result.