Bioinspired Computational Approach to Missing Value Estimation

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 ha...

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Bibliographic Details
Published in:Mathematical problems in engineering 2018-01, Vol.2018 (2018), p.1-16
Main Authors: Yang, Hongji, Fong, Simon, Millham, Richard, Agbehadji, Israel Edem
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Big Data
Biomimetics
Birds
Cleaning
Cooperative learning
Data analysis
Data management
Datasets
Decision making
Estimates
Evolutionary algorithms
Information science
International conferences
Maximum likelihood method
Missing data
Neural networks
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2018/9457821