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: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Penerbit UMP
2016
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| Subjects: | |
| Online Access: | 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 http://umpir.ump.edu.my/id/eprint/13837/ http://ijsecs.ump.edu.my/images/archive/vol2/0017.pdf |
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| Summary: | 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.
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