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Erschienen in: Neuroinformatics 2/2018

21.03.2018 | Original Article

Fast, Accurate, and Stable Feature Selection Using Neural Networks

verfasst von: James Deraeve, William H. Alexander

Erschienen in: Neuroinformatics | Ausgabe 2/2018

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Abstract

Multi-voxel pattern analysis often necessitates feature selection due to the high dimensional nature of neuroimaging data. In this context, feature selection techniques serve the dual purpose of potentially increasing classification accuracy and revealing sets of features that best discriminate between classes. However, feature selection techniques in current, widespread use in the literature suffer from a number of deficits, including the need for extended computational time, lack of consistency in selecting features relevant to classification, and only marginal increases in classifier accuracy. In this paper we present a novel method for feature selection based on a single-layer neural network which incorporates cross-validation during feature selection and stability selection through iterative subsampling. Comparing our approach to popular alternative feature selection methods, we find increased classifier accuracy, reduced computational cost and greater consistency with which relevant features are selected. Furthermore, we demonstrate that importance mapping, a technique used to identify voxels relevant to classification, can lead to the selection of irrelevant voxels due to shared activation patterns across categories. Our method, owing to its relatively simple architecture, flexibility and speed, can provide a viable alternative for researchers to identify sets of features that best discriminate classes.

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Metadaten
Titel
Fast, Accurate, and Stable Feature Selection Using Neural Networks
verfasst von
James Deraeve
William H. Alexander
Publikationsdatum
21.03.2018
Verlag
Springer US
Erschienen in
Neuroinformatics / Ausgabe 2/2018
Print ISSN: 1539-2791
Elektronische ISSN: 1559-0089
DOI
https://doi.org/10.1007/s12021-018-9371-3

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