TY - JOUR
T1 - A new selective neural network ensemble with negative correlation
AU - Lee, Heesung
AU - Kim, Euntai
AU - Pedrycz, Witold
N1 - Publisher Copyright:
© Springer Science+Business Media, LLC 2012.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2012/12/1
Y1 - 2012/12/1
N2 - An ensemble of neural networks exhibits higher generalization performance compared to a single neural network. In this paper, a new design method for a neural network ensemble is proposed. The hierarchical pair competition-based parallel genetic algorithm (HFC-PGA) is employed to train the neural networks forming the ensemble. The aim of the HFC-PGA is to achieve not only the best neural network, but also a diversity of potential neural networks. A set of component neural networks is selected to build an ensemble such that the generalization error is minimized and the negative correlation is maximized. Finally, some experiments are carried out using several data sets to illustrate and quantify the performance of the proposed method.
AB - An ensemble of neural networks exhibits higher generalization performance compared to a single neural network. In this paper, a new design method for a neural network ensemble is proposed. The hierarchical pair competition-based parallel genetic algorithm (HFC-PGA) is employed to train the neural networks forming the ensemble. The aim of the HFC-PGA is to achieve not only the best neural network, but also a diversity of potential neural networks. A set of component neural networks is selected to build an ensemble such that the generalization error is minimized and the negative correlation is maximized. Finally, some experiments are carried out using several data sets to illustrate and quantify the performance of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=84857820925&partnerID=8YFLogxK
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U2 - 10.1007/s10489-012-0342-3
DO - 10.1007/s10489-012-0342-3
M3 - Article
AN - SCOPUS:84857820925
VL - 37
SP - 488
EP - 498
JO - Applied Intelligence
JF - Applied Intelligence
SN - 0924-669X
IS - 4
ER -