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Neuroinformatic Databases and Mining of Knowledge of Them
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SYSNO ASEP 0088987 Document Type M - Monograph Chapter R&D Document Type Monograph Chapter Title Tree-based Classification Models for Somnolence Detection from EEG Spectra Title Klasifikační modely založené na stromech pro detekci ospalosti ze spekter EEG Author(s) Klaschka, Jan (UIVT-O) RID, SAI, ORCID Source Title Neuroinformatic Databases and Mining of Knowledge of Them. - Prague : Czech Technical University, 2007 / Novák M. - ISBN 978-80-87136-01-0 Pages s. 212-233 Number of pages 22 s. Language eng - English Country CZ - Czech Republic Keywords classification trees ; classification forests ; random forests ; OOB estimates ; EEG classification ; somnolence ; microsleeps Subject RIV BB - Applied Statistics, Operational Research R&D Projects ME 701 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10300504 - UIVT-O (2005-2011) Annotation EEG spectra corresponding to the states of somnolence, wakefulness and mentation of 24 experimental subjects are analyzed by different tree-based methods. Classification forests obtained by the Random Forests (RF) method are clearly superior to the single trees grown by CART. Applying RF separately to the small data sets of individual subjects results in the "individual" models that outperform, in the mean, the "global" classifiers derived by RF from the more numerous but, at the same time, more heterogeneous data of all the subjects. The newly developed mixed models, combining information from both the individual and global models, prove slightly better than the individual models. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2008
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