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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
Citation: 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.
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
ISSN: 03772217
DOI: 10.1016/S0377-2217(02)00792-0
Appears in Collections:Staff Publications

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