The “peeking” effect in supervised feature selection on diffusion tensor imaging data
We read with great interest the article by Haller et al1 in the February 2013 issue of the
American Journal of Neuroradiology. The authors used whole-brain diffusion tensor imaging–
derived fractional anisotropy (FA) data, skeletonized through use of the standard tract-based
spatial statistics (TBSS) pipeline, to achieve the following: 1) report significant group
differences in FA among mild cognitive impairment (MCI) subtypes, and 2) perform
individual classification of MCI subtypes by using a supervised feature selection procedure …
American Journal of Neuroradiology. The authors used whole-brain diffusion tensor imaging–
derived fractional anisotropy (FA) data, skeletonized through use of the standard tract-based
spatial statistics (TBSS) pipeline, to achieve the following: 1) report significant group
differences in FA among mild cognitive impairment (MCI) subtypes, and 2) perform
individual classification of MCI subtypes by using a supervised feature selection procedure …
[引用][C] The" Peeking" Effect in Supervised Feature Selection on Diffusion Tensor Imaging Data REPLY
The "Peeking" Effect in Supervised Feature Selection on Diffusion Tensor Imaging Data
REPLY … The "Peeking" Effect in Supervised Feature Selection on Diffusion Tensor
Imaging Data REPLY … Title The "Peeking" Effect in Supervised Feature Selection on
Diffusion Tensor Imaging Data REPLY …
REPLY … The "Peeking" Effect in Supervised Feature Selection on Diffusion Tensor
Imaging Data REPLY … Title The "Peeking" Effect in Supervised Feature Selection on
Diffusion Tensor Imaging Data REPLY …
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