Number of the records: 1
Neuroinformatic Databases and Mining of Knowledge of Them
- 1.
SYSNO ASEP 0088983 Document Type M - Monograph Chapter R&D Document Type Monograph Chapter Title Using Fuzzy k-NN Ensembles in EEG Data Classification Title Kombinování Fuzzy k-NN klasifikátorů pro klasifikaci EEG dat Author(s) Štefka, David (UIVT-O)
Holeňa, Martin (UIVT-O) SAI, RIDSource Title Neuroinformatic Databases and Mining of Knowledge of Them. - Prague : Czech Technical University, 2007 / Novák M. - ISBN 978-80-87136-01-0 Pages s. 200-211 Number of pages 12 s. Language eng - English Country CZ - Czech Republic Keywords EEG data ; classification ; classifier combining ; quality improvement ; extracting knowledge ; fuzzy k-nearest neighbor classifiers Subject RIV IN - Informatics, Computer Science R&D Projects 1ET100300517 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) 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". 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2008
Number of the records: 1