Deep learning-bat high-dimensional missing data estimator

Collins Leke, A. R. Ndjiongue, Bhekisipho Twala, Tshilidzi Marwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In recent years, several new methods for missing data estimation have been developed. Real world datasets possess the properties of big data being 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 paper, 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 was experimentally tested and compared against other existing methods on an off-line dataset. The results obtained have shown promising potential with slightly longer execution times, which are a worthy tradeoff when accuracy is of importance.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages483-488
Number of pages6
ISBN (Electronic)9781538616451
DOIs
Publication statusPublished - 27 Nov 2017
Event2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 - Banff, Canada
Duration: 5 Oct 20178 Oct 2017

Publication series

Name2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Volume2017-January

Conference

Conference2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Country/TerritoryCanada
CityBanff
Period5/10/178/10/17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Control and Optimization

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