Fault diagnosis based on pruned ensemble

Author

Sun, Jian ; Li, Leijun ; Hu, Qinghua

Author_Institution

Harbin Inst. of Technol., Harbin, China

Volume

1

fYear

2012

fDate

15-17 July 2012

Firstpage

35

Lastpage

40

Abstract

A new fault diagnosis method based on ensemble pruning is proposed. Ensemble pruning means to search for a good subset of ensemble members that performs as well as, or better than, the original ensemble. Margin distribution on training sets is thought as an important factor to improve the generalization performance of classifiers. In this paper, based on the margin loss minimization, a new ensemble pruning algorithm is proposed and utilized in fault diagnosis. Experiment results show the effectiveness of the proposed technique.

Keywords

fault diagnosis; generalisation (artificial intelligence); learning (artificial intelligence); minimisation; pattern classification; classifier generalization performance; ensemble member; ensemble pruning algorithm; fault diagnosis method; margin distribution; margin loss minimization; training sets; Abstracts; Artificial neural networks; Ensemble pruning; classification confidence; fault diagnosis; margin loss;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2012 International Conference on

Conference_Location

Xian

ISSN

2160-133X

Print_ISBN

978-1-4673-1484-8

Type

conf

DOI

10.1109/ICMLC.2012.6358882

Filename

6358882