BibTeX
@MISC{Setiono01abstract,
author = {Rudy Setiono and James Y. L. Thong},
title = {Abstract},
year = {2001}
}
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Abstract
An Approach to Generate Rules from Neural Networks for Regression Problems 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. Experimental results show that the proposed approach generates rules that are more accurate than the existing methods based on decision trees and linear regression. The approach is illustrated with three examples on various application problems.
Keyphrases
regression problem neural network linear regression research work classification problem many application relevant input attribute extracted rule regression problem artificial neural network prior knowledge various application problem trained neural network symbolic rule decision tree business application problem linear function data sample experimental result network output data distribution
