title: The utility of data-driven feature selection: re: Chu et al. 2012.
abstract: The recent Chu et al. (2012) manuscript discusses two key findings regarding feature selection (FS): (1) data driven FS was no better than using whole brain voxel data and (2) a priori biological knowledge was effective to guide FS. Use of FS is highly relevant in neuroimaging-based machine learning, as the number of attributes can greatly exceed the number of exemplars. We strongly endorse their demonstration of both of these findings, and we provide additional important practical and theoretical arguments as to why, in their case, the data-driven FS methods they implemented did not result in improved accuracy. Further, we emphasize that the data-driven FS methods they tested performed approximately as well as the all-voxel case. We discuss why a sparse model may be favored over a complex one with similar performance. We caution readers that the findings in the Chu et al. report should not be generalized to all data-driven FS methods.
authors.affiliation: David Geffen School of Medicine at UCLA, USA. Electronic address: wesleytk@ucla.edu.
authors.forename: Wesley T
authors.initials: WT
authors.lastname: Kerr
authors.forename: Pamela K
authors.initials: PK
authors.lastname: Douglas
authors.forename: Ariana
authors.initials: A
authors.lastname: Anderson
authors.forename: Mark S
authors.initials: MS
authors.lastname: Cohen
mesh_terms: D000544:Alzheimer Disease / Q000145:classification* / Q000473:pathology*; D060825:Cognitive Dysfunction / Q000145:classification* / Q000473:pathology*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D059906:Neuroimaging
keywords: Feature selection; Machine learning; Neuroimaging
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