Bagging of complementary neural networks with double dynamic weight averaging

S. Nakkrasae; P. Kraipeerapun; S. Amornsamankul; C.C. Fung

doi:10.1109/SNPD.2010.34

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Bagging of complementary neural networks with double dynamic weight averaging

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Bagging of complementary neural networks with double dynamic weight averaging

S. Nakkrasae, P. Kraipeerapun, S. Amornsamankul and C.C. Fung

2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp.173-178

IEEE

Software Engineering Artificial Intelligence Networking and Parallel/Distributed Computing (SNPD), 2010 11th ACIS International Conference (London, 09/06/2010–11/06/2010)

2010

DOI: https://doi.org/10.1109/SNPD.2010.34

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Abstract

Ensemble technique has been widely applied in regression problems. This paper proposes a novel approach of the ensemble of Complementary Neural Network (CMTNN) using double dynamic weight averaging. In order to enhance the diversity in the ensemble, different training datasets created based on bagging technique are applied to an ensemble of pairs of feed-forward back-propagation neural networks created to predict the level of truth and falsity values. In order to obtain more accuracy, uncertainties in the prediction of truth and falsity values are used to weight the prediction results in two steps. In the first step, the weight is used to average the truth and the falsity values whereas the weight in the second step is used to calculate the final regression output. The proposed approach has been tested with benchmarking UCI data sets. The results derived from our technique improve the prediction performance while compared to the traditional ensemble of neural networks which is predicted based on only the truth values. Furthermore, the obtained results from our novel approach outperform the results from the existing ensemble of complementary neural network.

Details

Title: Bagging of complementary neural networks with double dynamic weight averaging
Authors/Creators: S. Nakkrasae (Author/Creator) - Ramkhamhaeng University
P. Kraipeerapun (Author/Creator) - Ramkhamhaeng University
S. Amornsamankul (Author/Creator) - Mahidol University
C.C. Fung (Author/Creator) - Murdoch University
Publication Details: 2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp.173-178
Conference: Software Engineering Artificial Intelligence Networking and Parallel/Distributed Computing (SNPD), 2010 11th ACIS International Conference (London, 09/06/2010–11/06/2010)
Publisher: IEEE
Identifiers: 991005545030607891
Murdoch Affiliation: School of Information Technology
Language: English
Resource Type: Conference paper
Note: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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