The "peeking" effect in supervised feature selection on diffusion tensor imaging data

American Journal of Neuroradiology

Volumn 34, Issue 9, 2013, Pages

  Diciotti, S ^a   Ciulli, S ^a   Mascalchi, M ^a   Giannelli, M ^b   Toschi, N ^c  

^a UNIVERSITY OF FLORENCE   (Italy)

^b UNIVERSITY HOSPITAL OF PISA   (Italy)

^c UNIVERSITY OF ROME TOR VERGATA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALZHEIMER DISEASE; BRAIN; CLASSIFIER; DATA ANALYSIS; DIFFUSION TENSOR IMAGING; FRACTIONAL ANISOTROPY; LETTER; MILD COGNITIVE IMPAIRMENT; SUPPORT VECTOR MACHINE;

DIFFUSION TENSOR IMAGING; FEMALE; HUMANS; MALE; MILD COGNITIVE IMPAIRMENT; NERVE FIBERS, MYELINATED; SUPPORT VECTOR MACHINES;

EID: 85047689356     PISSN: 01956108     EISSN: 1936959X     Source Type: Journal    
DOI: 10.3174/ajnr.A3685     Document Type: Letter

References (4)

1. Haller S, Missoraiier P, Herrmann FR, et al. Individual classification of mild cognitive impairment subtypes by support vector machine analysis of white matter DTI. AJNR Am J Neuroradiol 2013;34:283-91
2. Pereira F, Mitchell T, Botvinick M. Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 2009;45(1 suppl):S199-209
3. Smialowski P, Frishman D, Kramer S. Pitfalls of supervised feature selection. Bioinformatics 2010;26:440-43
4. Diciotti S, Ginestroni A, Bessi V, et al. Identification of mild Alzheimer's disease through automated classification of structural MRI features. Conf Proc IEEE Eng Med Biol Soc 2012;2012:428-31