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

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. (Published)

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DOI/Official URL: http://ijsecs.ump.edu.my/images/archive/vol2/0017....

Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Feed-forward network, generalized regression, machine learning, prediction
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Faculty/Division:	Faculty of Computer System And Software Engineering
Depositing User:	Mrs. Neng Sury Sulaiman
Date Deposited:	04 Aug 2016 07:22
Last Modified:	17 Jan 2022 01:30
URI:	http://umpir.ump.edu.my/id/eprint/13837
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