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Combining least-squares support vector machines for classification of biomedical signals: a case study with knee-joint vibroarthrographic signals

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The knee-joint vibroarthrographic (VAG) signal could be used as an indicator with regard to the degenerative articular cartilage surfaces of the knee. Computer-aided analysis of VAG signals could provide quantitative indices for the noninvasive diagnosis of knee-joint pathologies at different stages. In this article, we propose a novel multiple classifier system (MCS) based on a recurrent neural network (RNN), to classify a dataset of 89 knee-joint VAG signals. The MCS consists of a group of component classifiers in the form of the least-squares support vector machine. The knowledge generated by the component classifiers is combined with the linear and normalised fusion model, the weights of which are optimised during the energy convergence process of the RNN. The experimental results showed that the proposed MCS was able to provide the classification accuracy of 80.9% and the area of 0.9484 under the receiver operating characteristics curve. The diagnostic performance of the MCS was superior to that obtained with the prevailing fusion approaches, such as the majority vote, the simple average and the median average.
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Keywords: ensemble learning; fusion; knee-joint vibration; least-squares support vector machine; multiple classifier systems; pattern recognition; vibroarthrography

Document Type: Research Article

Affiliations: Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, M5B 2K3, Canada

Publication date: March 1, 2011

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