loading...
K-Nearest Oracle for Dynamic Ensemble Selection
Ninth International Conference on Doc ...
 This Article 
 
PDF
HTML
IEEE Xplore Subscribers
 
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
A.H.-R. Ko, University of Quebec
R. Sabourin, University of Quebec
A. Britto Jr., Pontifical Catholic University of Parana
For handwritten pattern recognition, multiple classifier system has been shown to be useful in improving recognition rates. One of the most important issues to optimize a mul- tiple classifier system is to select a group of adequate clas- sifiers, known as Ensemble of Classifiers (EoC), from a pool of classifiers. Static selection schemes select an EoC for all test patterns, and dynamic selection schemes select different classifiers for different test patterns. Nevertheless, it has been shown that traditional dynamic selection does not give bet- ter performance than static selection. We propose four new dynamic selection schemes which explore the property of the oracle concept. The result suggests that the proposed schemes are apparently better than the static selection using the major- ity voting rule for combining classifiers.
Citation:
A.H.-R. Ko, R. Sabourin, A. Britto Jr., "K-Nearest Oracle for Dynamic Ensemble Selection," icdar,pp.422-426, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 1, 2007
Usage of this product signifies your acceptance of the Terms of Use.


Click here to go to beta feedback form