Paper Info
Title
Dynamic and static weighting in classifier fusion
Abstract
When a Multiple Classifier System is employed, one of the most popular methods to accomplish the classifier fusion is the simple majority voting. However, when the performance of the ensemble members is not uniform, the efficiency of this type of voting is affected negatively. In this paper, a comparison between simple and weighted voting (both dynamic and static) is presented. New weighting methods, mainly in the direction of the dynamic approach, are also introduced. Experimental results with several real-problem data sets demonstrate the advantages of the weighting strategies over the simple voting scheme. When comparing the dynamic and the static approaches, results show that the dynamic weighting is superior to the static strategy in terms of classification accuracy.
Year
DOI
Venue
2005
10.1007/11492542_8
IbPRIA (2)
Keywords
Field
DocType
weighting strategy,dynamic approach,weighted voting,simple majority voting,static weighting,new weighting method,classifier fusion,multiple classifier system,static strategy,static approach,dynamic weighting,simple voting scheme,majority voting
Data set,Weighting,Voting,Pattern recognition,Computer science,Image processing,Weighted voting,Sensor fusion,Artificial intelligence,Classifier (linguistics),Majority rule
Conference
Volume
ISSN
ISBN
3523
0302-9743
3-540-26154-0
Citations 
PageRank 
References 
9
0.74
13
Authors
3
Name
Order
Citations
PageRank
R. M. Valdovinos119313.67
J. Salvador Sánchez213914.01
R Barandela355823.46