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Reseach Article

Destructive Learning Analysis and Constructive Algorithm for Rule Extraction based on a Trained Neural Network using Gene Expression Programming

by Marghny H. Mohamed, Yasmeen T. Mahmoud, Saad Z. Rida
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 17
Year of Publication: 2014
Authors: Marghny H. Mohamed, Yasmeen T. Mahmoud, Saad Z. Rida

Marghny H. Mohamed, Yasmeen T. Mahmoud, Saad Z. Rida . Destructive Learning Analysis and Constructive Algorithm for Rule Extraction based on a Trained Neural Network using Gene Expression Programming. International Journal of Computer Applications. 89, 17 ( March 2014), 18-26. DOI=10.5120/15723-4602

@article{ 10.5120/15723-4602,
author = { Marghny H. Mohamed, Yasmeen T. Mahmoud, Saad Z. Rida },
title = { Destructive Learning Analysis and Constructive Algorithm for Rule Extraction based on a Trained Neural Network using Gene Expression Programming },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 17 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 18-26 },
numpages = {9},
url = { },
doi = { 10.5120/15723-4602 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:09:30.103086+05:30
%A Marghny H. Mohamed
%A Yasmeen T. Mahmoud
%A Saad Z. Rida
%T Destructive Learning Analysis and Constructive Algorithm for Rule Extraction based on a Trained Neural Network using Gene Expression Programming
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 17
%P 18-26
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

The present paper introduces destructive neural network learning techniques and presents the analysis of the convergence rate of the error in a neural network with and without threshold. Also, a constructive algorithm for rule extraction based on a trained neural network using Gene Expression Programming (GEP) is proposed. The rules are not an easy task due to the large number of examples entered to the input layer. Thus, we can use GEP to encode the rules in the form of logic expression. Finally, the proposed model is evaluated on different public-domain datasets and compared with standard learning models from WEKA, and then the results accentuate that the set of rules extraction from the proposed method is more accurate and brief compared with those achieved by the other models.

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Index Terms

Computer Science
Information Sciences


Neural Network Destructive Learning Constructive Learning Pruning Rule Extraction Classification Rules Gene Expression Programming.