Missing Data Estimation Using Bat Algorithm

Collins Achepsah Leke, Tshilidzi Marwala

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)


In recent years, several methods for missing data estimation have been developed. Real-world datasets possess the properties of big data such as volume, velocity and variety. With an increase in volume which includes sample size and dimensionality, existing imputation methods have become less effective and accurate. Much attention has been given to narrow artificial intelligence frameworks courtesy of their efficiency in low-dimensional settings. However, with an increase in dimensionality, these methods yield unrepresentative imputations with an impact on decision-making processes. Therefore, in this chapter, we present a new framework for missing data imputation in high-dimensional datasets. A deep learning technique is used in conjunction with a swarm intelligence algorithm. The performance of the proposed technique is experimentally tested and compared against other existing methods on an off-line dataset. The results obtained showed promising potential with slightly longer execution times, which are a worthy trade-off when accuracy is of importance.

Original languageEnglish
Title of host publicationStudies in Big Data
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
Publication statusPublished - 2019

Publication series

NameStudies in Big Data
ISSN (Print)2197-6503
ISSN (Electronic)2197-6511

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Engineering (miscellaneous)
  • Computer Science Applications
  • Artificial Intelligence


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