DocumentCode :
2708171
Title :
Weighted voting-based ensemble classifiers with application to human face recognition and voice recognition
Author :
Mu, Xiaoyan ; Lu, Jiangfeng ; Watta, Paul ; Hassoun, Mohamad H.
Author_Institution :
Electr. & Comput. Eng. Dept., Rose-Human Inst. of Technol., Terre Haute, IN, USA
fYear :
2009
fDate :
14-19 June 2009
Firstpage :
2168
Lastpage :
2171
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.
Keywords :
face recognition; image classification; speech recognition; human face recognition; human voice recognition; identification rate; pattern recognition; weighted plurality voting combination strategy; weighted voting-based ensemble classifier; Analytical models; Character recognition; Face recognition; Humans; Neural networks; Pattern recognition; Performance analysis; Speech recognition; Stochastic systems; Voting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
ISSN :
1098-7576
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2009.5178708
Filename :
5178708
Link To Document :
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