Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 488-498 |
| Number of pages | 11 |
| Journal | Applied Intelligence |
| Volume | 37 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2012 Dec 1 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
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A new selective neural network ensemble with negative correlation. / Lee, Heesung; Kim, Euntai; Pedrycz, Witold.
In: Applied Intelligence, Vol. 37, No. 4, 01.12.2012, p. 488-498.Research output: Contribution to journal › Article
TY - JOUR
T1 - A new selective neural network ensemble with negative correlation
AU - Lee, Heesung
AU - Kim, Euntai
AU - Pedrycz, Witold
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
UR - http://www.scopus.com/inward/citedby.url?scp=84857820925&partnerID=8YFLogxK
U2 - 10.1007/s10489-012-0342-3
DO - 10.1007/s10489-012-0342-3
M3 - Article
VL - 37
SP - 488
EP - 498
JO - Applied Intelligence
JF - Applied Intelligence
SN - 0924-669X
IS - 4
ER -