Paper Info
Title
Weighted voting-based ensemble classifiers with application to human face recognition and voice recognition
Abstract
A recent trend in the field of pattern recognition has been the use of ensemble classifiers. If combined properly, the ensemble can achieve a higher identification rate than any individual classifier. Plurality voting is one of the most commonly used combination strategies. The performance of plurality voting can be improved if the decisions of different classifiers are weighted properly. In this paper, we both theoretically and experimentally analyze the performance of a weighted plurality voting combination strategy to combine the decisions of multiple classifiers. Theoretical expressions characterizing the performance of the weighted voting model are derived and the method is applied to the problem of human face recognition and voice recognition. The results show the advantage of employing weighted-voting-based ensemble classifiers in achieving high identification rates.
Year
DOI
Venue
2009
10.1109/IJCNN.2009.5178708
IJCNN
Keywords
Field
DocType
weighted voting model,weighted plurality voting combination,combination strategy,pattern recognition,human face recognition,voice recognition,plurality voting,ensemble classifier,high identification rate,weighted voting-based ensemble classifier,weighted-voting-based ensemble classifier,data mining,neural networks,face,face recognition,image classification,speech recognition,voting,mathematical model
Expression (mathematics),Computer science,Random subspace method,Weighted voting,Artificial intelligence,Contextual image classification,Classifier (linguistics),Facial recognition system,Pattern recognition,Cascading classifiers,Speech recognition,Plurality voting,Machine learning
Conference
ISSN
Citations 
PageRank 
2161-4393
2
0.40
References 
Authors
6
4
Name
Order
Citations
PageRank
Xiaoyan Mu1292.88
Jiangfeng Lu220.40
Paul Watta3506.16
Mohamad H. Hassoun4789.14