Using an Enhanced Feed-Forward BP Network for Predictive Model Building From Students’ Data
Feed-forward, Back Propagation (BP) Network is a network structure capable of modeling the class prediction as a nonlinear combination of the inputs. The network has proven its suitability in solving several complex tasks, most especially when trained with appropriate algorithms. This study presen...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis
2015
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/12850/1/Using%20an%20Enhanced%20Feed%20Forward%20BP%20Network%20for%20Predictive%20Model%20Building%20from%20Students%20Data.pdf http://umpir.ump.edu.my/id/eprint/12850/ http://dx.doi.org/10.1080/10798587.2015.1079364 |
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| Summary: | Feed-forward, Back Propagation (BP) Network is a network structure capable of modeling the class
prediction as a nonlinear combination of the inputs. The network has proven its suitability in solving
several complex tasks, most especially when trained with appropriate algorithms. This study presents
an enhancement of this network with a view to boosting its prediction accuracy. The paper proposed a
modification of the data partitioning function in the regular feed-forward network. A predictive model
is constructed based on the proposed partition, while the second model is based on the partition of
the existing network. Both models are trained and simulated with sets of untrained data. The mean
absolute error is computed for both models and their error values are compared. Comparison of their
results shows that the enhanced network consistently delivers higher accuracy and generalized better
than the existing network in its regular structure; as there was a decrease in error from 0.261 to 0.016.
The enhanced network has also shown its suitability in the fittings of models from students’ data for
prediction purposes. |
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