The use of genetic algorithms and neural networks to approximate missing data in database

Mussa Abdella, Tshilidzi Marwala

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

Missing data creates various problems in analysing and processing data in databases. In this paper we introduce a new method aimed at approximating missing data in a database using a combination of genetic algorithms and neural networks. The proposed method uses genetic algorithm to minimise an error function derived from an auto-associative neural network. Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) networks are employed to train the neural net-works. Our focus also lies on the investigation of using the proposed method in accurately predicting missing data as the number of missing cases within a single record increases. It is observed that there is no significant reduction in accuracy of results as the number of missing cases in a single record increases. It is also found that results obtained using RBF are superior to MLP.

Original languageEnglish
Pages (from-to)577-589
Number of pages13
JournalComputing and Informatics
Volume24
Issue number6
Publication statusPublished - 2005
Externally publishedYes

Keywords

  • Auto-associative
  • Error function
  • Genetic algorithms
  • Missing data
  • Multi-layer perceptron
  • Neural networks
  • Radial basis function

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computational Theory and Mathematics

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