Skip to main content
eScholarship
Open Access Publications from the University of California

The utility of data-driven feature selection: Re: Chu et al. 2012

  • Author(s): Kerr, WT
  • Douglas, PK
  • Anderson, A
  • Cohen, MS
  • et al.
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, andwe provide additional important practical and theoretical arguments as towhy, 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. © 2013 Elsevier Inc.

Many UC-authored scholarly publications are freely available on this site because of the UC Academic Senate's Open Access Policy. Let us know how this access is important for you.

Main Content
Current View