INVESTIGATING THE EFFECT OF DATA NORMALIZATION ON PREDICTIVE MODELS
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
Faculty of Communication and Information Sciences
Abstract
The creation of predictive model using a supervised learning approach involves the task of building a
model of the target variable as a function of the explanatory variables. Before a model is created, it is
necessary to put the data in a suitable format. Studies have shown that normalization of data is crucial to
descriptive mining as it improve the accuracy and efficiency of mining algorithms. However, in the case
of prediction, it is not in all cases that predictive models are created from normalized data. This paper
presents the experimental results of investigating the effect of normalizing the input variables on models
created for prediction purposes. Experiments are conducted for the creation of predictive models from
two different sets of equal size of data using neural network techniques. The trained network models
created with the same architecture and configurations are subsequently simulated using a set of
untrained data. The evaluation results and the comparison of the models created through the two data
sets of different format reveals that, the model created from a normalized data appears to be more
accurate as a decrease in error by 0.003 are consistently recorded. The model also converges much
earlier than the model created from the data that does not undergo any form of normalization.
Description
Main article
Keywords
data normalization, data pre-processing, predictive model, supervised learning
Citation
International Journal of Information Processing and Communication