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Published in:
2010 | OriginalPaper | Chapter
Choosing Parameters for Random Subspace Ensembles for fMRI Classification
Authors : Ludmila I. Kuncheva, Catrin O. Plumpton
Published in: Multiple Classifier Systems
Publisher: Springer Berlin Heidelberg
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Functional magnetic resonance imaging (fMRI) is a non-invasive and powerful method for analysis of the operational mechanisms of the brain. fMRI classification poses a severe challenge because of the extremely large feature-to-instance ratio. Random Subspace ensembles (RS) have been found to work well for such data. To enable a theoretical analysis of RS ensembles, we assume that only a small (known) proportion of the features are important to the classification, and the remaining features are noise. Three properties of RS ensembles are defined: usability, coverage and feature-set diversity. Their expected values are derived for a range of RS ensemble sizes (
L
) and cardinalities of the sampled feature subsets (
M
). Our hypothesis that larger values of the three properties are beneficial for RS ensembles was supported by a simulation study and an experiment with a real fMRI data set. The analyses suggested that RS ensembles benefit from medium
M
and relatively small
L
.