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Classification of EEG Data using Fuzzy k-NN Ensembles
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SYSNO ASEP 0087505 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Classification of EEG Data using Fuzzy k-NN Ensembles Title Klasifikace EEG dat s použitím kombinování Fuzzy k-NN klasifikátorů Author(s) Štefka, David (UIVT-O)
Holeňa, Martin (UIVT-O) SAI, RIDSource Title Informačné technológie - Aplikácie a Teória. - Seňa : PONT, 2007 / Vojtáš P. - ISBN 978-80-969184-6-1 Pages s. 91-94 Number of pages 4 s. Action ITAT 2007. Conference on Theory and Practice of Information Theory Event date 21.09.2007-27.09.2007 VEvent location Poľana Country SK - Slovakia Event type EUR Language eng - English Country SK - Slovakia Keywords ensemble methods ; classifier combining ; classifier fusion ; classifier aggregation ; Sugeno fuzzy integral Subject RIV IN - Informatics, Computer Science R&D Projects 1ET100300517 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR) GA201/05/0325 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10300504 - UIVT-O (2005-2011) Annotation Ensemble methods try to improve quality of classification by creating multiple classifiers and aggregating their outputs. In this paper, we present the use of ensemble methods for classification of EEG data from the project "Building Neuroinformation Bases, and Extracting Knowledge from them", within which a possibility of preventing drivers' microsleeps is studied. A multiple feature subset ensemble method is used to improve the quality of classification of a fuzzy k-nearest neighbor classifier. Two different aggregation schemes are used - the mean value aggregation algorithm outperforming the Sugeno fuzzy integral aggregation algorithm Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2008
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