Missing Data Estimation using Principle Component Analysis and Autoassociative Neural Networks
Three new methods are used for estimating missing data in a database using Neural Networks, Principal Component Analysis and Genetic Algorithms are presented. The proposed methods are tested on a set of data obtained from the South African Antenatal Survey. The data is a collection of demographic pr...
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International Institute of Informatics and Cybernetics
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Online Access: | http://www.iiisci.org/Journal/CV$/sci/pdfs/KS628XI.pdf
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doaj-b511bf05f94e4ba8949dd404285206cd2020-11-24T23:28:40ZengInternational Institute of Informatics and CyberneticsJournal of Systemics, Cybernetics and Informatics1690-45242009-06-01737279Missing Data Estimation using Principle Component Analysis and Autoassociative Neural NetworksJaisheel Mistry0Fulufhelo V. Nelwamondo1Tshilidzi Marwala2 University of Witwatersrand Council for Scientific and Industrial Research, CSIR University of Johannesburg Three new methods are used for estimating missing data in a database using Neural Networks, Principal Component Analysis and Genetic Algorithms are presented. The proposed methods are tested on a set of data obtained from the South African Antenatal Survey. The data is a collection of demographic properties of patients. The proposed methods use Principal Component Analysis to remove redundancies and reduce the dimensionality in the data. Variations of autoassociative Neural Networks are used to further reduce the dimensionality of the data. A Genetic Algorithm is then used to find the missing data by optimizing the error function of the three variants of the Autoencoder Neural Network. The proposed system was tested on data with 1 to 6 missing fields in a single record of data and the accuracy of the estimated values were calculated and recorded. All methods are as accurate as a conventional feedforward neural network structure however the use of the newly proposed methods employs neural network architectures that have fewer hidden nodes.http://www.iiisci.org/Journal/CV$/sci/pdfs/KS628XI.pdf Missing DataAuto Associative Neural NetworkAutoencoder Neural NetworksPrincipal Component AnalysisGenetic Algorithm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jaisheel Mistry Fulufhelo V. Nelwamondo Tshilidzi Marwala |
spellingShingle |
Jaisheel Mistry Fulufhelo V. Nelwamondo Tshilidzi Marwala Missing Data Estimation using Principle Component Analysis and Autoassociative Neural Networks Journal of Systemics, Cybernetics and Informatics Missing Data Auto Associative Neural Network Autoencoder Neural Networks Principal Component Analysis Genetic Algorithm |
author_facet |
Jaisheel Mistry Fulufhelo V. Nelwamondo Tshilidzi Marwala |
author_sort |
Jaisheel Mistry |
title |
Missing Data Estimation using Principle Component Analysis and Autoassociative Neural Networks |
title_short |
Missing Data Estimation using Principle Component Analysis and Autoassociative Neural Networks |
title_full |
Missing Data Estimation using Principle Component Analysis and Autoassociative Neural Networks |
title_fullStr |
Missing Data Estimation using Principle Component Analysis and Autoassociative Neural Networks |
title_full_unstemmed |
Missing Data Estimation using Principle Component Analysis and Autoassociative Neural Networks |
title_sort |
missing data estimation using principle component analysis and autoassociative neural networks |
publisher |
International Institute of Informatics and Cybernetics |
series |
Journal of Systemics, Cybernetics and Informatics |
issn |
1690-4524 |
publishDate |
2009-06-01 |
description |
Three new methods are used for estimating missing data in a database using Neural Networks, Principal Component Analysis and Genetic Algorithms are presented. The proposed methods are tested on a set of data obtained from the South African Antenatal Survey. The data is a collection of demographic properties of patients. The proposed methods use Principal Component Analysis to remove redundancies and reduce the dimensionality in the data. Variations of autoassociative Neural Networks are used to further reduce the dimensionality of the data. A Genetic Algorithm is then used to find the missing data by optimizing the error function of the three variants of the Autoencoder Neural Network. The proposed system was tested on data with 1 to 6 missing fields in a single record of data and the accuracy of the estimated values were calculated and recorded. All methods are as accurate as a conventional feedforward neural network structure however the use of the newly proposed methods employs neural network architectures that have fewer hidden nodes. |
topic |
Missing Data Auto Associative Neural Network Autoencoder Neural Networks Principal Component Analysis Genetic Algorithm |
url |
http://www.iiisci.org/Journal/CV$/sci/pdfs/KS628XI.pdf
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work_keys_str_mv |
AT jaisheelmistry missingdataestimationusingprinciplecomponentanalysisandautoassociativeneuralnetworks AT fulufhelovnelwamondo missingdataestimationusingprinciplecomponentanalysisandautoassociativeneuralnetworks AT tshilidzimarwala missingdataestimationusingprinciplecomponentanalysisandautoassociativeneuralnetworks |
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