Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0377-2217(02)00792-0
Title: An approach to generate rules from neural networks for regression problems
Authors: Setiono, R. 
Thong, J.Y.L.
Keywords: Curve fitting
Knowledge-based systems
Machine learning
Neural networks
Nonlinear regression
Issue Date: 2004
Source: Setiono, R., Thong, J.Y.L. (2004). An approach to generate rules from neural networks for regression problems. European Journal of Operational Research 155 (1) : 239-250. ScholarBank@NUS Repository. https://doi.org/10.1016/S0377-2217(02)00792-0
Abstract: Artificial neural networks have been successfully applied to a variety of business application problems involving classification and regression. They are especially useful for regression problems as they do not require prior knowledge about the data distribution. In many applications, it is desirable to extract knowledge from trained neural networks so that the users can gain a better understanding of the solution. Existing research works have focused primarily on extracting symbolic rules for classification problems with few methods devised for regression problems. In order to fill this gap, we propose an approach to extract rules from neural networks that have been trained to solve regression problems. The extracted rules divide the data samples into groups. For all samples within a group, a linear function of the relevant input attributes of the data approximates the network output. The approach is illustrated with two examples on various application problems. Experimental results show that the proposed approach generates rules that are more accurate than the existing methods based on decision trees and linear regression. © 2003 Elsevier B.V. All rights reserved.
Source Title: European Journal of Operational Research
URI: http://scholarbank.nus.edu.sg/handle/10635/42479
ISSN: 03772217
DOI: 10.1016/S0377-2217(02)00792-0
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

30
checked on Jan 17, 2018

WEB OF SCIENCETM
Citations

23
checked on Dec 20, 2017

Page view(s)

586
checked on Jan 15, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.