Missing Data Estimation Using Invasive Weed Optimization Algorithm

Collins Achepsah Leke, Tshilidzi Marwala

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

3 Citations (Scopus)


In this chapter, we further amalgamate a deep learning framework and swarm intelligence for missing data estimation in high-dimensional datasets. Missing data is a recurrent issue in day-to-day datasets, resulting in a variety of setbacks which are often difficult for existing techniques which constitute narrow artificial intelligence architectures and computational intelligence methods. This is normally aligned with dimensionality and the number of rows. We propose a framework for the imputation procedure that uses a deep learning method with a swarm intelligence algorithm called deep learning-invasive weed optimization (DL-IWO) approach.

Original languageEnglish
Title of host publicationStudies in Big Data
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
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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