Variance-covariance based weighing for neural network ensembles

Neural network (NN) is a popular artificial intelligence technique for solving complicated problems due to their inherent capabilities. However generalization in NN can be harmed by a number of factors including parameter's initialization, inappropriate network topology and setting parameters o...

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Main Authors: Hassan, S., Khosravi, A., Jaafar, J.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.32523 /
Published: 2013
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893616999&doi=10.1109%2fSMC.2013.548&partnerID=40&md5=aa07a4a882d3d2060e6e4f3b254b3dbb
http://eprints.utp.edu.my/32523/
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spelling utp-eprints.325232022-03-29T14:04:52Z Variance-covariance based weighing for neural network ensembles Hassan, S. Khosravi, A. Jaafar, J. Neural network (NN) is a popular artificial intelligence technique for solving complicated problems due to their inherent capabilities. However generalization in NN can be harmed by a number of factors including parameter's initialization, inappropriate network topology and setting parameters of the training process itself. Forecast combinations of NN models have the potential for improved generalization and lower training time. A weighted averaging based on Variance-Covariance method that assigns greater weight to the forecasts producing lower error, instead of equal weights is practiced in this paper. While implementing the method, combination of forecasts is done with all candidate models in one experiment and with the best selected models in another experiment. It is observed during the empirical analysis that forecasting accuracy is improved by combining the best individual NN models. Another finding of this study is that reducing the number of NN models increases the diversity and, hence, accuracy. © 2013 IEEE. 2013 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893616999&doi=10.1109%2fSMC.2013.548&partnerID=40&md5=aa07a4a882d3d2060e6e4f3b254b3dbb Hassan, S. and Khosravi, A. and Jaafar, J. (2013) Variance-covariance based weighing for neural network ensembles. In: UNSPECIFIED. http://eprints.utp.edu.my/32523/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Neural network (NN) is a popular artificial intelligence technique for solving complicated problems due to their inherent capabilities. However generalization in NN can be harmed by a number of factors including parameter's initialization, inappropriate network topology and setting parameters of the training process itself. Forecast combinations of NN models have the potential for improved generalization and lower training time. A weighted averaging based on Variance-Covariance method that assigns greater weight to the forecasts producing lower error, instead of equal weights is practiced in this paper. While implementing the method, combination of forecasts is done with all candidate models in one experiment and with the best selected models in another experiment. It is observed during the empirical analysis that forecasting accuracy is improved by combining the best individual NN models. Another finding of this study is that reducing the number of NN models increases the diversity and, hence, accuracy. © 2013 IEEE.
format Conference or Workshop Item
author Hassan, S.
Khosravi, A.
Jaafar, J.
spellingShingle Hassan, S.
Khosravi, A.
Jaafar, J.
Variance-covariance based weighing for neural network ensembles
author_sort Hassan, S.
title Variance-covariance based weighing for neural network ensembles
title_short Variance-covariance based weighing for neural network ensembles
title_full Variance-covariance based weighing for neural network ensembles
title_fullStr Variance-covariance based weighing for neural network ensembles
title_full_unstemmed Variance-covariance based weighing for neural network ensembles
title_sort variance-covariance based weighing for neural network ensembles
publishDate 2013
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893616999&doi=10.1109%2fSMC.2013.548&partnerID=40&md5=aa07a4a882d3d2060e6e4f3b254b3dbb
http://eprints.utp.edu.my/32523/
_version_ 1741197745439375360
score 11.62408

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