Number of the records: 1  

Neuroinformatic Databases and Mining of Knowledge of Them

  1. 1.
    SYSNO ASEP0088983
    Document TypeM - Monograph Chapter
    R&D Document TypeMonograph Chapter
    TitleUsing Fuzzy k-NN Ensembles in EEG Data Classification
    TitleKombinování Fuzzy k-NN klasifikátorů pro klasifikaci EEG dat
    Author(s) Štefka, David (UIVT-O)
    Holeňa, Martin (UIVT-O) SAI, RID
    Source TitleNeuroinformatic Databases and Mining of Knowledge of Them. - Prague : Czech Technical University, 2007 / Novák M. - ISBN 978-80-87136-01-0
    Pagess. 200-211
    Number of pages12 s.
    Languageeng - English
    CountryCZ - Czech Republic
    KeywordsEEG data ; classification ; classifier combining ; quality improvement ; extracting knowledge ; fuzzy k-nearest neighbor classifiers
    Subject RIVIN - Informatics, Computer Science
    R&D Projects1ET100300517 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR)
    ME 701 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
    GA201/05/0325 GA ČR - Czech Science Foundation (CSF)
    CEZAV0Z10300504 - UIVT-O (2005-2011)
    AnnotationEnsemble 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". The EEG data are classified using different algorithms from the Weka framework to find out an efficient classification algorithm for the EEG data. A multiple feature subset ensemble method is then 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.
    WorkplaceInstitute of Computer Science
    ContactTereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
    Year of Publishing2008
Number of the records: 1  

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