loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
17th International Conference on Pattern Recognition (ICPR'04) - Volume 1
Simulating Classifier Ensembles of Fixed Diversity for Studying Plurality Voting Performance
Cambridge UK
August 23-August 26
ISBN: 0-7695-2128-2
ASCII Text
x 
 
H?la Zouari, Laurent Heutte, Yves Lecourtier, Adel Alimi, "Simulating Classifier Ensembles of Fixed Diversity for Studying Plurality Voting Performance," Pattern Recognition, International Conference on, vol. 1, pp. 232-235, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 1, 2004.
 
 
BibTex
x
 
@article{ 10.1109/ICPR.2004.1334066,
author = {H?la Zouari and Laurent Heutte and Yves Lecourtier and Adel Alimi},
title = {Simulating Classifier Ensembles of Fixed Diversity for Studying Plurality Voting Performance},
journal ={Pattern Recognition, International Conference on},
volume = {1},
year = {2004},
issn = {1051-4651},
pages = {232-235},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1334066},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pattern Recognition, International Conference on
TI - Simulating Classifier Ensembles of Fixed Diversity for Studying Plurality Voting Performance
SN - 1051-4651
SP232
EP235
A1 - H?la Zouari,
A1 - Laurent Heutte,
A1 - Yves Lecourtier,
A1 - Adel Alimi,
PY - 2004
KW - null
VL - 1
JA - Pattern Recognition, International Conference on
ER -
 
H?la Zouari, Universit? de Rouen, France
Laurent Heutte, Universit? de Rouen, France
Yves Lecourtier, Universit? de Rouen, France
Adel Alimi, Universit? de Sfax, Tunisie
This paper presents a new method for the artificial generation of classifier outputs in order to analyse the performance of plurality voting according both to the accuracies of the combined classifiers and to the agreement among them. This analysis is conducted in parallel with majority voting in order to compare the efficiency of these two methods when combining dependent classifiers. The experimental results show that the plurality voting is more efficient in achieving the trade-off between rejection rate and recognition rate.
Citation:
H?la Zouari, Laurent Heutte, Yves Lecourtier, Adel Alimi, "Simulating Classifier Ensembles of Fixed Diversity for Studying Plurality Voting Performance," icpr, vol. 1, pp.232-235, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 1, 2004
Usage of this product signifies your acceptance of the Terms of Use.