Clustered sampling improves random subspace brain mapping
Pattern Recognition
Kernel clustering using a hybrid memetic algorithm
Natural Computing: an international journal
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We propose a Memetic algorithm for feature selection in volumetric data containing spatially distributed clusters of informative features, typically encountered in neuroscience applications. The proposed method complements a conventional genetic algorithm with a local search utilizing inherent spatial relationships to efficiently identify informative feature clusters across multiple regions of the search volume. First, we demonstrate the utility of the algorithm on simulated data containing informative feature clusters of varying contrast-to-noise-ratios. The Memetic algorithm identified a majority of the relevant features whereas a conventional genetic algorithm detected only a subset sufficient for fitness maximization. Second, we applied the algorithm to authentic functional magnetic resonance imaging (fMRI) brain activity data from a motor task study, where the Memetic algorithm identified expected brain regions and subsequent brain activity prediction in new individuals was accurate at an average of 76% correct classification. The proposed algorithm constitutes a novel method for efficient volumetric feature selection and is applicable in any 3D data scenario. In particular, the algorithm is a promising alternative for sensitive brain activity mapping and decoding.