Correction for model selection bias using a modified model averaging approach for supervised learning methods applied to EEG experiments.
This paper proposes a modified model averaging approach for linear discriminant analysis. This approach is used in combination with a doubly hierarchical supervised learning analysis and applied to preclinical pharmaco-electroencephalographical data for classification of psychotropic drugs. Classification of a test dataset was highly improved with this method.
Wouters
Kristien
K
Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Universiteit Hasselt, Diepenbeek, Belgium. wouters.kristien@gmail.com
Abrahantes
Jose Cortinas
JC
Molenberghs
Geert
G
Geys
Helena
H
Ahnaou
Abdellah
A
Drinkenburg
Wilhelmus H I M
WH
Bijnens
Luc
L
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D016002:Discriminant Analysis; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D004569:Electroencephalography / Q000187:drug effects*; D016013:Likelihood Functions; D016014:Linear Models; D015233:Models, Statistical; D011619:Psychotropic Drugs / Q000145:classification / Q000494:pharmacology*; D051381:Rats; D012044:Regression Analysis; D015983:Selection Bias; D012894:Sleep Stages / Q000187:drug effects; D014851:Wakefulness / Q000187:drug effects
D011619:Psychotropic Drugs