An Approach To Generate Rules From Neural Networks for Regression Problems
European Journal of Operational Research, 155, 1 (2004), pp. 239-250.
16 Pages Posted: 23 Mar 2021 Last revised: 4 Jun 2021
Date Written: 2004
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.
Keywords: Neural Networks, Nonlinear Regression, Curve Fitting, Machine Learning, Knowledge-Based Systems
JEL Classification: C45, M15
Suggested Citation: Suggested Citation