Knowledge of extraction from trained neural network by using decision tree

Inside the sets of data, hidden knowledge can be acquired by using neural network. These knowledge are described within topology, using activation function and connection weight at hidden neurons and output neurons. Is hardly to be understanding since neural networks act as a black box. The black bo...

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Main Authors:	Soleh, Ardiansyah, Mazlina, Abdul Majid, Jasni, Mohamad Zain
Format:	Conference or Workshop Item
Language:	English English
Published:	IEEE 2017
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/18263/ http://umpir.ump.edu.my/id/eprint/18263/ http://umpir.ump.edu.my/id/eprint/18263/1/Knowledge%20of%20Extraction%20from%20Trained%20Neural%20Network%20by%20Using%20Decision%20Tree.pdf http://umpir.ump.edu.my/id/eprint/18263/2/Knowledge%20of%20Extraction%20from%20Trained%20Neural%20Network%20by%20Using%20Decision%20Tree%201.pdf

id	ump-18263
recordtype	eprints
spelling	ump-182632018-07-19T07:16:28Z http://umpir.ump.edu.my/id/eprint/18263/ Knowledge of extraction from trained neural network by using decision tree Soleh, Ardiansyah Mazlina, Abdul Majid Jasni, Mohamad Zain QA76 Computer software Inside the sets of data, hidden knowledge can be acquired by using neural network. These knowledge are described within topology, using activation function and connection weight at hidden neurons and output neurons. Is hardly to be understanding since neural networks act as a black box. The black box problem can be solved by extracting knowledge (rule) from trained neural network. Thus, the aim of this paper is to extract valuable information from trained neural networks using decision. Further, the Levenberg Marquardt algorithm was applied to training 30 networks for each datasets, using learning parameters and basis weights differences. As the number of hidden neurons increase, mean squared error and mean absolute percentage error decrease, and more time they need to deal with the dataset, that is result of investigation from neural network architectures. Decision tree induction generally performs better in knowledge extraction result with accuracy and precision level from 84.07 to 93.17 percent. The extracted rule can be used to explaining the process of the neural network systems and also can be applied in other systems like expert systems. IEEE 2017-02 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/18263/1/Knowledge%20of%20Extraction%20from%20Trained%20Neural%20Network%20by%20Using%20Decision%20Tree.pdf pdf en http://umpir.ump.edu.my/id/eprint/18263/2/Knowledge%20of%20Extraction%20from%20Trained%20Neural%20Network%20by%20Using%20Decision%20Tree%201.pdf Soleh, Ardiansyah and Mazlina, Abdul Majid and Jasni, Mohamad Zain (2017) Knowledge of extraction from trained neural network by using decision tree. In: 2nd International Conference on Science in Information Technology (ICSITech 2016), 26-27 October 2016 , Balikpapan, Indonesia. pp. 220-225.. ISBN 978-1-5090-1721-8 https://ieeexplore.ieee.org/document/7852637/
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Soleh, Ardiansyah Mazlina, Abdul Majid Jasni, Mohamad Zain Knowledge of extraction from trained neural network by using decision tree
description	Inside the sets of data, hidden knowledge can be acquired by using neural network. These knowledge are described within topology, using activation function and connection weight at hidden neurons and output neurons. Is hardly to be understanding since neural networks act as a black box. The black box problem can be solved by extracting knowledge (rule) from trained neural network. Thus, the aim of this paper is to extract valuable information from trained neural networks using decision. Further, the Levenberg Marquardt algorithm was applied to training 30 networks for each datasets, using learning parameters and basis weights differences. As the number of hidden neurons increase, mean squared error and mean absolute percentage error decrease, and more time they need to deal with the dataset, that is result of investigation from neural network architectures. Decision tree induction generally performs better in knowledge extraction result with accuracy and precision level from 84.07 to 93.17 percent. The extracted rule can be used to explaining the process of the neural network systems and also can be applied in other systems like expert systems.
format	Conference or Workshop Item
author	Soleh, Ardiansyah Mazlina, Abdul Majid Jasni, Mohamad Zain
author_facet	Soleh, Ardiansyah Mazlina, Abdul Majid Jasni, Mohamad Zain
author_sort	Soleh, Ardiansyah
title	Knowledge of extraction from trained neural network by using decision tree
title_short	Knowledge of extraction from trained neural network by using decision tree
title_full	Knowledge of extraction from trained neural network by using decision tree
title_fullStr	Knowledge of extraction from trained neural network by using decision tree
title_full_unstemmed	Knowledge of extraction from trained neural network by using decision tree
title_sort	knowledge of extraction from trained neural network by using decision tree
publisher	IEEE
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/18263/ http://umpir.ump.edu.my/id/eprint/18263/ http://umpir.ump.edu.my/id/eprint/18263/1/Knowledge%20of%20Extraction%20from%20Trained%20Neural%20Network%20by%20Using%20Decision%20Tree.pdf http://umpir.ump.edu.my/id/eprint/18263/2/Knowledge%20of%20Extraction%20from%20Trained%20Neural%20Network%20by%20Using%20Decision%20Tree%201.pdf
first_indexed	2023-09-18T22:25:46Z
last_indexed	2023-09-18T22:25:46Z
_version_	1777415955892142080

Knowledge of extraction from trained neural network by using decision tree

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