Abstract
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.
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Kuncheva, L.I., Plumpton, C.O. (2010). Choosing Parameters for Random Subspace Ensembles for fMRI Classification. In: El Gayar, N., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2010. Lecture Notes in Computer Science, vol 5997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12127-2_6
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DOI: https://doi.org/10.1007/978-3-642-12127-2_6
Publisher Name: Springer, Berlin, Heidelberg
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