Comparing performances of neural network models built through transformed and original data
Data transformation (normalization) is a method used in data preprocessing to scale the range of values in the data within a uniform scale to improve the quality of the data; as a result, the prediction accuracy is improved. However, some scholars have questioned the efficacy of data normalizati...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/44526/ http://irep.iium.edu.my/44526/ http://irep.iium.edu.my/44526/1/I4CT.pdf http://irep.iium.edu.my/44526/4/44526_Comparing%20performances%20of%20neural_Scopus.pdf |
| Summary: | Data transformation (normalization) is a method
used in data preprocessing to scale the range of values in the data
within a uniform scale to improve the quality of the data; as a
result, the prediction accuracy is improved. However, some
scholars have questioned the efficacy of data normalization,
arguing that it can destroy the structure in the original (raw)
data. To address these arguments, we compared the prediction
performances of the two methods in the domain of crude oil
prices due to its global significance. It was found that the
multilayer perceptron neural network model that was built using
normalized data significantly outperformed the multilayer
perceptron neural network that was built using raw data. The
number of iterations and the computation time for both of the
methods were statistically equal as well as for the regression. In
view of the arguments in the literature about data
standardization, the results of this research could allow
researchers in the domain of crude oil price prediction to choose
the best opinion. |
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