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Mathematical Problems in Engineering
Volume 2018, Article ID 9457821, 16 pages
https://doi.org/10.1155/2018/9457821
Research Article

Bioinspired Computational Approach to Missing Value Estimation

1ICT and Society Research Group, Department of Information Technology, Durban University of Technology, Durban, South Africa
2Department of Computer and Information Science, University of Macau, Taipa, Macau
3Department of Computer and Information Science, Bath Spa University, Bath, UK

Correspondence should be addressed to Richard C. Millham; moc.liamtoh@mahllimdrahcir

Received 26 June 2017; Revised 16 November 2017; Accepted 13 December 2017; Published 2 January 2018

Academic Editor: Erik Cuevas

Copyright © 2018 Israel Edem Agbehadji et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Missing data occurs when values of variables in a dataset are not stored. Estimating these missing values is a significant step during the data cleansing phase of a big data management approach. The reason of missing data may be due to nonresponse or omitted entries. If these missing data are not handled properly, this may create inaccurate results during data analysis. Although a traditional method such as maximum likelihood method extrapolates missing values, this paper proposes a bioinspired method based on the behavior of birds, specifically the Kestrel bird. This paper describes the behavior and characteristics of the Kestrel bird, a bioinspired approach, in modeling an algorithm to estimate missing values. The proposed algorithm (KSA) was compared with WSAMP, Firefly, and BAT algorithm. The results were evaluated using the mean of absolute error (MAE). A statistical test (Wilcoxon signed-rank test and Friedman test) was conducted to test the performance of the algorithms. The results of Wilcoxon test indicate that time does not have a significant effect on the performance, and the quality of estimation between the paired algorithms was significant; the results of Friedman test ranked KSA as the best evolutionary algorithm.