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	An Adaptive Classifier Fusion Method for Analysis of Knee-Joint Vibroarthrographic Signals
	Wu, Y. F. ( Department of Electrical and Computer Engineering, Ryerson University) ; Krishnan, S. ( Department of Electrical and Computer Engineering, Ryerson University)
刊名	http://dx.doi.org/10.1109/cimsa.2009.5069945
	2013-12-12
关键词	RADIAL-BASIS FUNCTIONS CARTILAGE PATHOLOGY
英文摘要	Externally recorded knee-joint vibroarthrographic (VAG) signals bear diagnostic information related to degenerative conditions of cartilage disorders in a knee. In this paper, the number of atoms derived from wavelet matching pursuit (MP) decomposition and the parameter of turns count with the fixed threshold that characterizes the waveform variability of VAG signals were extracted for computer-aided analysis. A novel multiple classifier system (MCS) based on the adaptive weighted fusion (AWF) method is proposed for the classification of VAG signals. The experimental results shows that the proposed AWF-based MCS is able to provide the classification accuracy of 80.9%, and the area of 0.8674 under the receiver operating characteristic curve over the data set of 89 VAG signals. Such results are superior to those obtained with best component classifier in the form of least-squares support vector machine, and the popular Bagging ensemble method.
语种	英语
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/70739]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Wu, Y. F. ,Krishnan, S. . An Adaptive Classifier Fusion Method for Analysis of Knee-Joint Vibroarthrographic Signals[J]. http://dx.doi.org/10.1109/cimsa.2009.5069945,2013.
APA	Wu, Y. F. ,&Krishnan, S. .(2013).An Adaptive Classifier Fusion Method for Analysis of Knee-Joint Vibroarthrographic Signals.http://dx.doi.org/10.1109/cimsa.2009.5069945.
MLA	Wu, Y. F. ,et al."An Adaptive Classifier Fusion Method for Analysis of Knee-Joint Vibroarthrographic Signals".http://dx.doi.org/10.1109/cimsa.2009.5069945 (2013).

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