Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18595
Title: Classification of bio-potential surface electrode based on FKCM and SVM
Authors: Liu, H
Tao, X 
Xu, P
Qiu, G
Keywords: Biopotential surface electrode
Classification
FKCM
Recognition
SVM
Issue Date: 2011
Source: Journal of software, 2011, v. 6, no. 5, p. 880-886 How to cite?
Journal: Journal of Software 
Abstract: In this paper, a method which is used for evaluating the performance of bio-potential surface electrode (BSE) with multi-index is presented. The Fuzzy kernel C-means (FKCM) algorithm and KF statistic are employed for classifying the BSE samples and searching an optimal classification amount respectively. Subsequently, a discriminant function is constructed by support vector machines (SVM) for recognizing the new measured samples. Experimental result shows classification correction ratios of improved FKCM algorithm are 96.3% and 85% on the IRIS and BSE dataset according a priori knowledge, furthermore, the recognition correction ratios of SVM algorithm are 96.3% and 90% on the IRIS and BSE dataset.
URI: http://hdl.handle.net/10397/18595
ISSN: 1796-217X
DOI: 10.4304/jsw.6.5.880-886
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