EVALUATING THE EFFECT OF DATASET SIZE ON PREDICTIVE MODEL USING SUPERVISED LEARNING TECHNIQUE

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.

Authors and Affiliations

A. R. Ajiboye, Abdullah Arshah, H. Qin

Keywords

Related Articles

THE IMPACTS OF SOCIAL NETWORKING SITES IN HIGHER LEARNING

Social networking sites, a web-based application have permeated the boundary between personal lives and student lives. Nowadays, students in higher learning used social networking site such as Facebook to facilitate thei...

PARALLEL INTEGRATION ALGORITHM AND ITS USAGE FOR A PRACTICAL SIMULATION OF SPACECRAFT ATTITUDE MOTION

Nowadays multi-core processors are installed almost in each modern workstation, but the question of these computational resources effective utilization is still a topical one. In this paper the four-point block one-step...

EFFECTS OF VIDEO DISPLAY TERMINAL RESOLUTIONS TO THE LEGIBILITY OF TEXT ON A WEB PAGE

Higher Video Display Terminal (VDT) resolutions have been proven to provide better quality in improving image quality displayed. The higher the resolution means more pixels per-inch-square available to display an image....

METAMODELLING APPROACH AND SOFTWARE TOOLS FOR PHYSICAL MODELLING AND SIMULATION

In computer science, metamodelling approach becomes more and more popular for the purpose of software systems development. In this paper, we discuss applicability of the metamodelling approach for development of software...

IMPLEMENTING COMBINED FSM WITH CPLDS

The subject of the research in this article is the logic circuit of the combined finite state machine (CFSM), which combines the functions of the both FSM Mealy and Moore. In practice, such a model of control automata is...

Download PDF file
  • EP ID EP254080
  • DOI -
  • Views 155
  • Downloads 0

How To Cite

A. R. Ajiboye, Abdullah Arshah, H. Qin (2015). EVALUATING THE EFFECT OF DATASET SIZE ON PREDICTIVE MODEL USING SUPERVISED LEARNING TECHNIQUE. International Journal of Software Engineering and Computer Systems, 1(1), 75-84. https://europub.co.uk/articles/-A-254080