Extraction of Rules from Artificial Neural Networks for Nonlinear Regression

Extraction of Rules from Artificial Neural Networks for Nonlinear Regression (2002)

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by Rudy Setiono , Wee Kheng Leow , Jacek M. Zurada

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Datum	Value	Source
TITLE	Extraction of Rules from Artificial Neural Networks for Nonlinear Regression	user correction - Legacy Corrections
AUTHOR NAME	Rudy Setiono	user correction - Legacy Corrections
AUTHOR AFFIL	School of Computing; National University of Singapore	user correction - Legacy Corrections
AUTHOR ADDR	Kent Ridge, Singapore 119260	user correction - Legacy Corrections
AUTHOR NAME	Wee Kheng Leow	user correction - Legacy Corrections
AUTHOR AFFIL	School of Computing; National University of Singapore	user correction - Legacy Corrections
AUTHOR ADDR	Kent Ridge, Singapore 119260	user correction - Legacy Corrections
AUTHOR NAME	Jacek M. Zurada	user correction - Legacy Corrections
AUTHOR AFFIL	Dept. of Electrical and Computer Engineering; University of Louisville	user correction - Legacy Corrections
AUTHOR ADDR	Louisville, KY 40208, USA	user correction - Legacy Corrections
ABSTRACT	Neural networks have been successfully applied to solve a variety of application problems including classification and function approximation. They are especially useful as function approximators because they do not require prior knowledge of the input data distribution and they have been shown to be universal approximators. In many applications, it is desirable to extract knowledge that can explain how the problems are solved by the networks. Most existing approaches have focused on extracting symbolic rules for classification. Few methods have been devised to extract rules from trained neural networks for regression. This article presents an approach for extracting rules from trained neural networks for regression. Each rule in the extracted rule set corresponds to a subregion of the input space and a linear function involving the relevant input attributes of the data approximates the network output for all data samples in this subregion. Extensive experimental results on 32 benchmark data sets demonstrate the effectiveness of the proposed approach in generating accurate regression rules.	user correction - Legacy Corrections
YEAR	2002	user correction - Legacy Corrections
CITATIONS	35 found	ParsCit 1.0