The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Random subspace method for multivariate feature selection
Pattern Recognition Letters
Semi-random subspace method for face recognition
Image and Vision Computing
Clustered sampling improves random subspace brain mapping
Pattern Recognition
Online semi-supervised ensemble updates for fMRI data
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
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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.