Fuzzy support vector machine with joint optimization of genetic algorithm and fuzzy c-means.
Background: Motor imagery electroencephalogram (MI-EEG) play an important role in the field of neurorehabilitation, and a fuzzy support vector machine (FSVM) is one of the most used classifiers. Specifically, a fuzzy c-means (FCM) algorithm was used to membership calculation to deal with the classif...
| Publicado en: | Technology & Health Care Vol. 29; no. 5; pp. 921 - 938 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Sage Publications Inc.
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152820826&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152820826 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09287329 3QT jtl: Technology & Health Care issn: 09287329 maglogo: N pubinfo: dt: 2021 vid: 29 iid: 5 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 152820826 152820826 NLM33459673 152820826 10.3233/THC-202619 NLM33459673 152820826 ppf: 921 ppct: 17 formats: tig: atl: Fuzzy support vector machine with joint optimization of genetic algorithm and fuzzy c-means. aug: au: Li, Ming-Ai Wang, Ruo-Tu Wei, Li-Na affil: Faculty of Information Technology, Beijing University of Technology, Beijing, China sug: subj: Electroencephalography Support Vector Machine Genetic Algorithms Physics Logic ab: Background: Motor imagery electroencephalogram (MI-EEG) play an important role in the field of neurorehabilitation, and a fuzzy support vector machine (FSVM) is one of the most used classifiers. Specifically, a fuzzy c-means (FCM) algorithm was used to membership calculation to deal with the classification problems with outliers or noises. However, FCM is sensitive to its initial value and easily falls into local optima.Objective: The joint optimization of genetic algorithm (GA) and FCM is proposed to enhance robustness of fuzzy memberships to initial cluster centers, yielding an improved FSVM (GF-FSVM).Method: The features of each channel of MI-EEG are extracted by the improved refined composite multivariate multiscale fuzzy entropy and fused to form a feature vector for a trial. Then, GA is employed to optimize the initial cluster center of FCM, and the fuzzy membership degrees are calculated through an iterative process and further applied to classify two-class MI-EEGs.Results: Extensive experiments are conducted on two publicly available datasets, the average recognition accuracies achieve 99.89% and 98.81% and the corresponding kappa values are 0.9978 and 0.9762, respectively.Conclusion: The optimized cluster centers of FCM via GA are almost overlapping, showing great stability, and GF-FSVM obtains higher classification accuracies and higher consistency as well. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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