Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Related work
 3. Suggested method
 4. Experiments
 5. Conclusions
 References

키워드

저자

• Hyontai Sug [ Division of Computer & Information Engineering, Dongseo University, Busan, 617-716, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605