Missing Data Estimation Using Cuckoo Search Algorithm

Collins Achepsah Leke, Tshilidzi Marwala

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

2 Citations (Scopus)


This chapter brings together two related areas: deep learning and swarm intelligence for missing data estimation in high-dimensional datasets. The growing number of studies in the deep learning area warrants a closer look at its possible application in the domain. Missing data being an unavoidable scenario in present-day datasets results in different challenges, which are nontrivial for existing techniques that constitute narrow artificial intelligence architectures and computational intelligence methods. This can be attributed to the large number of samples and high number of features. In this chapter, we propose a new framework for the imputation procedure that uses a deep learning method with a swarm intelligence algorithm, called deep learning-cuckoo search (DL-CS). This technique is compared to similar approaches and other existing methods. The time required to obtain accurate estimates for the missing data entries surpasses that of existing methods, but this is considered a worthy bargain when the accuracy of the said estimates in a high-dimensional setting is taken into consideration.

Original languageEnglish
Title of host publicationStudies in Big Data
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
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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