DocumentCode :
2922060
Title :
Combining multiple support vector machines for boosting the classification accuracy of uterine EMG signals
Author :
Moslem, Bassam ; Khalil, Mohamad ; Diab, Mohamad O. ; Chkeir, Aly ; Marque, Catherine
Author_Institution :
LASTRE Lab., Lebanese Univ., Tripoli, Lebanon
fYear :
2011
fDate :
11-14 Dec. 2011
Firstpage :
631
Lastpage :
634
Abstract :
A defining feature of physiological systems is the complexity both in their structures and functions. As a result, classifying physiological data is a difficult task. In this paper, we propose the use of a committee machines with a Support Vector Machines (SVM) as the component classifier in order to boost the classification accuracy of multichannel uterine electromyogram (EMG) signals. The approach was applied on each channel and a majority voting rule was used in order to determine the final decision of the committee. The results indicate that a committee machines exhibits performance unobtainable by an individual committee member on its own. We conclude that this approach can improve the recognition accuracy and has a competitive and promising performance.
Keywords :
biological organs; electromyography; image classification; medical image processing; physiological models; support vector machines; EMG signals; SVM; classification accuracy; image recognition; individual committee member; multichannel uterine electromyogram signals; multiple support vector machines; physiological data; physiological systems; Accuracy; Electromyography; Feature extraction; Kernel; Pregnancy; Support vector machines; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronics, Circuits and Systems (ICECS), 2011 18th IEEE International Conference on
Conference_Location :
Beirut
Print_ISBN :
978-1-4577-1845-8
Electronic_ISBN :
978-1-4577-1844-1
Type :
conf
DOI :
10.1109/ICECS.2011.6122354
Filename :
6122354
Link To Document :
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