Treatment of missing data using neural networks and genetic algorithms

Mussa Abdella, Tshilidzi Marwala

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

19 Citations (Scopus)

Abstract

This paper introduces a 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. An investigation on using the proposed method to accurately approximate missing data as the number of missing cases within a single record increases is conducted. Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are employed. Results obtained using RBF are found to be better than those from the MLP. Results from a combination of both MLP and RBF are found to be better than those obtained using either MLP or RBF individually.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages598-603
Number of pages6
DOIs
Publication statusPublished - 2005
Externally publishedYes
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: 31 Jul 20054 Aug 2005

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume1

Conference

ConferenceInternational Joint Conference on Neural Networks, IJCNN 2005
Country/TerritoryCanada
CityMontreal, QC
Period31/07/054/08/05

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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