EVALUATING THE EFFECT OF DATASET SIZE ON PREDICTIVE MODEL USING SUPERVISED LEARNING TECHNIQUE
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Date
2015-02
Journal Title
Journal ISSN
Volume Title
Publisher
Universiti Malaysia Pahang
Abstract
Learning models used for prediction purposes are mostly developed without paying much
cognizance to the size of datasets that can produce models of high accuracy and better
generalization. Although, the general believe is that, large dataset is needed to construct a
predictive learning model. To describe a data set as large in size, perhaps, is circumstance
dependent, thus, what constitutes a dataset to be considered as being big or small is vague. In this
paper, the ability of the predictive model to generalize with respect to a particular size of data
when simulated with new untrained input is examined. The study experiments on three different
sizes of data using Matlab program to create predictive models with a view to establishing if the
size of data has any effect on the accuracy of a model. The simulated output of each model is
measured using the Mean Absolute Error (MAE) and comparisons are made. Findings from this
study reveals that, the quantity of data partitioned for the purpose of training must be of good
representation of the entire sets and sufficient enough to span through the input space. The
results of simulating the three network models also shows that, the learning model with the
largest size of training sets appears to be the most accurate and consistently delivers a much
better and stable results.
Description
Article
Keywords
Neural Network, Prediction, Supervised Learning, Data mining, Data size
Citation
International Journal of Software Engineering & Computer Sciences