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Destructive Algorithm for Rule Extraction based on a Trained Neural Network

by M.e. Elalami
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 21
Year of Publication: 2012
Authors: M.e. Elalami
10.5120/5833-8119

M.e. Elalami . Destructive Algorithm for Rule Extraction based on a Trained Neural Network. International Journal of Computer Applications. 42, 21 ( March 2012), 8-14. DOI=10.5120/5833-8119

@article{ 10.5120/5833-8119,
author = { M.e. Elalami },
title = { Destructive Algorithm for Rule Extraction based on a Trained Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 21 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 8-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number21/5833-8119/ },
doi = { 10.5120/5833-8119 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:53.429175+05:30
%A M.e. Elalami
%T Destructive Algorithm for Rule Extraction based on a Trained Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 21
%P 8-14
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The present paper introduces a new destructive algorithm for rule extraction based on a trained neural network. The degree of complexity of neural network increases exponentially as a factor of the numbers of input and hidden nodes. Therefore, the dimensionality of the trained neural network is reduced by using a proposed destructive algorithm to extract only the most effective values of the input attributes which have higher impact on the output result for each class. Thus, the searching efficiency is highly increased and the computation is dramatically reduced for extracting rules. The generated rules from the proposed model are fired through two levels for each class. As for the first level, it deals with each individual effective input value, and the second level is concerned with each possible conjunction of the effective input values. Moreover, the proposed model extracts the strongest rules which represent a large number of instances from the database by adjusting the similarity measure threshold value. Finally, the proposed model is evaluated on different public-domain datasets and compared with standard learning models from WEKA, then the results assert that the set of rules extraction from the proposed method is more accurate and concise compared with those obtained by the other models.

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Index Terms

Computer Science
Information Sciences

Keywords

Rule Extraction Supervised Learning Neural Network Destructive Technique Performance Measures.

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