Comparing the Performance of Predictive Models Constructed Using the Techniques of Feed-Forword and Generalized Regression Neural Networks

Artificial Neural Network (ANNs) is an efficient machine learning method that can be used to fits model from data for prediction purposes. It is capable of modelling the class prediction as a nonlinear combination of the inputs. However, a number of factors may affect the accuracy of the model crea...

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Main Authors:	Ajiboye, Adeleke Raheem, Ruzaini, Abdullah Arshah, Hongwu, Qin, Jamila, Abdul Hadi
Format:	Article
Language:	English English
Published:	Penerbit UMP 2016
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/13837/ http://umpir.ump.edu.my/id/eprint/13837/ http://umpir.ump.edu.my/id/eprint/13837/ http://umpir.ump.edu.my/id/eprint/13837/1/Comparing%20The%20Performance%20Of%20Predictive%20Models%20Constructed%20Using%20The%20Techniques%20Of%20Feed-Forword%20And%20Generalized%20Regression%20Neural%20Networks.pdf http://umpir.ump.edu.my/id/eprint/13837/7/fskkp-2016-ajiboye-Comparing%20The%20Performance%20Of%20Predictive.pdf

id	ump-13837
recordtype	eprints
spelling	ump-138372018-04-27T03:20:30Z http://umpir.ump.edu.my/id/eprint/13837/ Comparing the Performance of Predictive Models Constructed Using the Techniques of Feed-Forword and Generalized Regression Neural Networks Ajiboye, Adeleke Raheem Ruzaini, Abdullah Arshah Hongwu, Qin Jamila, Abdul Hadi QA76 Computer software Artificial Neural Network (ANNs) is an efficient machine learning method that can be used to fits model from data for prediction purposes. It is capable of modelling the class prediction as a nonlinear combination of the inputs. However, a number of factors may affect the accuracy of the model created using this approach. The choice of network type and how the network is optimally configured plays important role in the performance of a predictive model created using neural network techniques. This paper compares the accuracy of two typical neural network techniques used for creating a predictive model. The techniques are feed-forward neural network and the generalized regression networks. The model created using both techniques are evaluated for correctness. The resulting outputs show that, the Generalized Regression Neural Network (GRNN) consistently produces a more accurate result. Findings further show that, the fitting of the network predictive model using the technique of Feed-forward Neural Network (FNN) records error value of 1.086 higher than the generalized regression network. Penerbit UMP 2016 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/13837/1/Comparing%20The%20Performance%20Of%20Predictive%20Models%20Constructed%20Using%20The%20Techniques%20Of%20Feed-Forword%20And%20Generalized%20Regression%20Neural%20Networks.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/13837/7/fskkp-2016-ajiboye-Comparing%20The%20Performance%20Of%20Predictive.pdf Ajiboye, Adeleke Raheem and Ruzaini, Abdullah Arshah and Hongwu, Qin and Jamila, Abdul Hadi (2016) Comparing the Performance of Predictive Models Constructed Using the Techniques of Feed-Forword and Generalized Regression Neural Networks. International Journal of Software Engineering & Computer Sciences (IJSECS), 2. pp. 66-73. ISSN 2289-8522 http://ijsecs.ump.edu.my/images/archive/vol2/0017.pdf DOI: 10.15282/ijsecs.2.2016.6.0017
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 Ajiboye, Adeleke Raheem Ruzaini, Abdullah Arshah Hongwu, Qin Jamila, Abdul Hadi Comparing the Performance of Predictive Models Constructed Using the Techniques of Feed-Forword and Generalized Regression Neural Networks
description	Artificial Neural Network (ANNs) is an efficient machine learning method that can be used to fits model from data for prediction purposes. It is capable of modelling the class prediction as a nonlinear combination of the inputs. However, a number of factors may affect the accuracy of the model created using this approach. The choice of network type and how the network is optimally configured plays important role in the performance of a predictive model created using neural network techniques. This paper compares the accuracy of two typical neural network techniques used for creating a predictive model. The techniques are feed-forward neural network and the generalized regression networks. The model created using both techniques are evaluated for correctness. The resulting outputs show that, the Generalized Regression Neural Network (GRNN) consistently produces a more accurate result. Findings further show that, the fitting of the network predictive model using the technique of Feed-forward Neural Network (FNN) records error value of 1.086 higher than the generalized regression network.
format	Article
author	Ajiboye, Adeleke Raheem Ruzaini, Abdullah Arshah Hongwu, Qin Jamila, Abdul Hadi
author_facet	Ajiboye, Adeleke Raheem Ruzaini, Abdullah Arshah Hongwu, Qin Jamila, Abdul Hadi
author_sort	Ajiboye, Adeleke Raheem
title	Comparing the Performance of Predictive Models Constructed Using the Techniques of Feed-Forword and Generalized Regression Neural Networks
title_short	Comparing the Performance of Predictive Models Constructed Using the Techniques of Feed-Forword and Generalized Regression Neural Networks
title_full	Comparing the Performance of Predictive Models Constructed Using the Techniques of Feed-Forword and Generalized Regression Neural Networks
title_fullStr	Comparing the Performance of Predictive Models Constructed Using the Techniques of Feed-Forword and Generalized Regression Neural Networks
title_full_unstemmed	Comparing the Performance of Predictive Models Constructed Using the Techniques of Feed-Forword and Generalized Regression Neural Networks
title_sort	comparing the performance of predictive models constructed using the techniques of feed-forword and generalized regression neural networks
publisher	Penerbit UMP
publishDate	2016
url	http://umpir.ump.edu.my/id/eprint/13837/ http://umpir.ump.edu.my/id/eprint/13837/ http://umpir.ump.edu.my/id/eprint/13837/ http://umpir.ump.edu.my/id/eprint/13837/1/Comparing%20The%20Performance%20Of%20Predictive%20Models%20Constructed%20Using%20The%20Techniques%20Of%20Feed-Forword%20And%20Generalized%20Regression%20Neural%20Networks.pdf http://umpir.ump.edu.my/id/eprint/13837/7/fskkp-2016-ajiboye-Comparing%20The%20Performance%20Of%20Predictive.pdf
first_indexed	2023-09-18T22:16:54Z
last_indexed	2023-09-18T22:16:54Z
_version_	1777415398100041728

Comparing the Performance of Predictive Models Constructed Using the Techniques of Feed-Forword and Generalized Regression Neural Networks

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