Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2002.1000125
Title: Extraction of rules from artificial neural networks for nonlinear regression
Authors: Setiono, R. 
Leow, W.K. 
Zurada, J.M.
Keywords: Network pruning
Regression
Rule extraction
Issue Date: 2002
Citation: Setiono, R., Leow, W.K., Zurada, J.M. (2002). Extraction of rules from artificial neural networks for nonlinear regression. IEEE Transactions on Neural Networks 13 (3) : 564-577. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2002.1000125
Abstract: Neural networks (NNs) 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 NNs for regression. This article presents an approach for extracting rules from trained NNs 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.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/42907
ISSN: 10459227
DOI: 10.1109/TNN.2002.1000125
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