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Description
  • 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 %22individual%22 models that outperform, in the mean, the %22global%22 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.
  • 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 %22individual%22 models that outperform, in the mean, the %22global%22 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. (en)
  • Spektra EEG odpovídající stavům ospalosti, bdělosti a mentace u 24 experimentálních subjektů jsou v práci analyzována různými metodami založenými na stromech. Klasifikační lesy získané metodou Random Forests (RF) jsou jednoznačně lepší než jednotlivé stromy sestrojené metodou CART. Aplikací metody RF na malé datové soubory jednotlivých subjektů vznikají %22individuální%22 modely, které jsou v průměru výrazně přesnější než %22globální%22 klasifikátory odvozené z početnějších, ale také heterogennějších dat všech subjektů. Nově vyvinuté smíšené modely, které kombinují informace z individuálních i globálních modelů, se ukazují jako ještě mírně lepší než individuální modely. (cs)
Title
  • Tree-based Classification Models for Somnolence Detection from EEG Spectra
  • Tree-based Classification Models for Somnolence Detection from EEG Spectra (en)
  • Klasifikační modely založené na stromech pro detekci ospalosti ze spekter EEG (cs)
skos:prefLabel
  • Tree-based Classification Models for Somnolence Detection from EEG Spectra
  • Tree-based Classification Models for Somnolence Detection from EEG Spectra (en)
  • Klasifikační modely založené na stromech pro detekci ospalosti ze spekter EEG (cs)
skos:notation
  • RIV/67985807:_____/07:00088987!RIV08-AV0-67985807
http://linked.open.../vavai/riv/strany
  • 212;233
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ME 701), Z(AV0Z10300504)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 455695
http://linked.open...ai/riv/idVysledku
  • RIV/67985807:_____/07:00088987
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • classification trees; classification forests; random forests; OOB estimates; EEG classification; somnolence; microsleeps (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [214E8F09ADD7]
http://linked.open...i/riv/mistoVydani
  • Prague
http://linked.open...vEdiceCisloSvazku
  • Edice monografií NNW, 7
http://linked.open...i/riv/nazevZdroje
  • Neuroinformatic Databases and Mining of Knowledge of Them
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Klaschka, Jan
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • České vysoké učení technické v Praze
https://schema.org/isbn
  • 978-80-87136-01-0
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