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Correction for Model Selection Bias Using a Modified Model Averaging Approach for Supervised Learning Methods Applied to EEG Experiments |
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| Authors: | Kristien Wouters José Cortiñas Abrahantes Geert Molenberghs Helena Geys Abdellah Ahnaou Wilhelmus H. I. M. Drinkenburg |
| Affiliation: | 1. Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Universiteit Hasselt , Diepenbeek, Belgium wouters.kristien@gmail.com;3. Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Universiteit Hasselt , Diepenbeek, Belgium;4. Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Universiteit Hasselt , Diepenbeek, Belgium;5. Johnson &6. Johnson Pharmaceutical Research and Development , Beerse, Belgium;7. Johnson & |
| Abstract: | 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. |
| Keywords: | EEG Fractional polynomials Linear discriminant analysis Linear mixed model Model average Supervised learning |