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

Mussa Abdella, Tshilidzi Marwala

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

90 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 Perception (MLP) and Radial Basis Function (RBF) networks are employed to train the neural networks. 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
Title of host publicationICCC 2005 - IEEE 3rd International Conference on Computational Cybernetics - Proceedings
Pages207-212
Number of pages6
DOIs
Publication statusPublished - 2005
Externally publishedYes
EventICCC 2005 - IEEE 3rd International Conference on Computational Cybernetics - Mauritius, Mauritius
Duration: 13 Apr 200516 Apr 2005

Publication series

NameICCC 2005 - IEEE 3rd International Conference on Computational Cybernetics - Proceedings
Volume2005

Conference

ConferenceICCC 2005 - IEEE 3rd International Conference on Computational Cybernetics
Country/TerritoryMauritius
CityMauritius
Period13/04/0516/04/05

ASJC Scopus subject areas

  • General Engineering

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