Neural Network Ensemble Based on Feature Selection

Author

Jian, Lin ; Bangzhu, Zhu

Author_Institution

Wuyi Univ., Jiangmen

fYear

2007

fDate

May 30 2007-June 1 2007

Firstpage

1844

Lastpage

1847

Abstract

In this paper, a novel neural network ensemble model, i.e. NNEIPCABag, which combines the feature selection technique, the improved principal component analysis (IPCA), with the Bagging method, is presented. Then the proposed model is employed for time series forecasting with the favor results obtained, which shows that the generalization ability of the proposed model can be superior to that of neural network ensemble only with the Bagging method, .i.e. NNEBag.

Keywords

neural nets; principal component analysis; time series; Bagging method; feature selection; generalization ability; improved principal component analysis; neural network ensemble model; time series forecasting; Bagging; Boosting; Diversity reception; Economic forecasting; Image coding; Neural networks; Predictive models; Principal component analysis; Training data; Voting; ecnmomic forecasting; feature selection; neural network ensemble;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Automation, 2007. ICCA 2007. IEEE International Conference on

Conference_Location

Guangzhou

Print_ISBN

978-1-4244-0818-4

Electronic_ISBN

978-1-4244-0818-4

Type

conf

DOI

10.1109/ICCA.2007.4376681

Filename

4376681

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2006828