International Journal of Electronic Engineering and Computer Science
Articles Information
International Journal of Electronic Engineering and Computer Science, Vol.5, No.1, Mar. 2020, Pub. Date: Feb. 14, 2020
An Automated Data Pre-processing Technique for Machine Learning in Critical Systems
Pages: 1-9 Views: 100 Downloads: 21
Authors
[01] Monica Madyembwa, Computer Science Department, National University of Science and Technology, Bulawayo, Zimbabwe.
[02] Kernan Mzelikahle, Computer Science Department, National University of Science and Technology, Bulawayo, Zimbabwe.
[03] Sibonile Moyo, Computer Science Department, National University of Science and Technology, Bulawayo, Zimbabwe.
Abstract
In many critical systems, the quality of data analysis is an important factor to consider particularly if the results of the data analysis contribute towards decision making. Data cleaning techniques are used during data preparation stage, before the application of data analysis techniques on a dataset. There is a strong causal relationship between quality of data preparation and quality of results in data analysis. For this reason, data cleaning techniques have a direct bearing on the quality of results from the data analysis stage. In this paper, we propose the use of intelligent data cleaning techniques as opposed to traditional deterministic methods. It is shown in this paper that the use of machine learning techniques to clean data, particularly as used for filling-in missing data, improves the quality of subsequent data analysis. Seven (7) flight-level datasets from the US Department of Transportation (Bureau of Transportation Statistics) were used to assess whether the quality of subsequent data analysis is significantly affected by the choice of a data pre-processing technique. A set of experiments were designed with an objective of conducting a comparative analysis of the performance of data analysis techniques on data prepared using different data cleaning techniques. Three (3) data analysis techniques, namely the LSTM, FFANN and RNN, were used in the comparative analysis study to determine how each of the techniques perform depending on the data cleaning technique used. The results obtained in the comparative study indicate that the use of machine learning techniques, such as BOSOM and K-means clustering, in data preparation, increases the quality of subsequent data analysis. The quality of data analysis was measured using performance metrics such as the Cross-Entropy loss and the Mean Square Error. Both assessment metrics show improved performance for each data analysis technique if data is cleaned using machine leaning methods.
Keywords
Long Short-Term Memory (LSTM), Bat Optimised Self Organised Map (BOSOM), Artificial Neural Networks, Data Pre-processing Techniques, K-Means Clustering
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