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Variance-covariance based weighing for neural network ensembles

conference contribution
posted on 2013-01-01, 00:00 authored by S Hassan, Abbas KhosraviAbbas Khosravi, J Jaafar
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.

History

Event

IEEE Systems, Man and Cybernetics. Conference (2013 : Manchester, England)

Pagination

3214 - 3219

Publisher

IEEE

Location

Manchester, England

Place of publication

Piscataway, N.J.

Start date

2013-10-13

End date

2013-10-16

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2013, IEEE

Title of proceedings

SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics