A dynamic programming approach to missing data estimation using neural networks

Fulufhelo V. Nelwamondo, Dan Golding, Tshilidzi Marwala

Research output: Contribution to journalArticlepeer-review

59 Citations (Scopus)

Abstract

This paper develops and presents a novel technique for missing data estimation using a combination of dynamic programming, neural networks and genetic algorithms (GA) on suitable subsets of the input data. The method proposed here is well suited for decision making processes and uses the concept of optimality and the Bellman's equation to estimate the missing data. The proposed approach is applied to an HIV/AIDS database and the results shows that the proposed method significantly outperforms a similar method where dynamic programming is not used. This paper also suggests a different way of formulating a missing data problem such that the dynamic programming is applicable to estimate the missing data.

Original languageEnglish
Pages (from-to)49-58
Number of pages10
JournalInformation Sciences
Volume237
DOIs
Publication statusPublished - 10 Jul 2013

Keywords

  • Data imputation
  • Dynamic programming
  • Genetic algorithms
  • Missing data
  • Neural networks

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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