Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81788
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dc.contributorDepartment of Health Technology and Informatics-
dc.creatorZhang, YPen_US
dc.creatorLi, XZen_US
dc.creatorZhu, JQen_US
dc.creatorWu, CYen_US
dc.creatorWu, QFen_US
dc.date.accessioned2020-02-10T12:29:12Z-
dc.date.available2020-02-10T12:29:12Z-
dc.identifier.issn2169-3536en_US
dc.identifier.urihttp://hdl.handle.net/10397/81788-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/en_US
dc.rightsThe following publication Y. Zhang, X. Li, J. Zhu, C. Wu and Q. Wu, "Epileptic EEG Signals Recognition Using a Deep View-Reduction TSK Fuzzy System With High Interpretability," in IEEE Access, vol. 7, pp. 137344-137354, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2942641en_US
dc.subjectMulti-view learningen_US
dc.subjectStacked generalization principleen_US
dc.subjectView reductionen_US
dc.subjectTSK fuzzy systemsen_US
dc.titleEpileptic EEG signals recognition using a deep view-Reduction TSK fuzzy system with high interpretabilityen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage137344en_US
dc.identifier.epage137354en_US
dc.identifier.volume7en_US
dc.identifier.doi10.1109/ACCESS.2019.2942641en_US
dcterms.abstractTakagi-Sugeno-Kang (TSK) fuzzy systems are well known for their good balance between approximation accuracy and interpretability. In this paper, we propose a deep view-reduction TSK fuzzy system termed as DVR-TSK-FS in which two powerful mechanisms associating with a deep structure are developed: 1) during the multi-view learning in each component, a sample-distribution-dependent parameter is defined to control the learning of the weight of each view. The parameter is not fixed by users, it is set according to the feature space in advance such that the learnt weight of each view indeed reflects the amount of pattern information involved in each view; 2) during the iteration of DRV-TSK-FS in each component, weak views are automatically reduced by comparing the learnt weight with a fixed threshold which is also automatically set according to the number of objects and the dimension of the feature space. 3) All components are linked in a stacked way based on the stacked generalization principle such that the outputs of all previous components are augmented into the current one which can help open the manifold structure of the original feature space. DRV-TSK-FS is testified on a multi-view EEG dataset for epileptic EEG signals recognition.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE access, 20 Sept. 2019, v. 7, p. 137344-137354en_US
dcterms.isPartOfIEEE accessen_US
dcterms.issued2019-
dc.identifier.isiWOS:000498708400001-
dc.identifier.scopus2-s2.0-85078248693-
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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