A comparison of four feature selection algorithms applied to multiply-imputed proteomic data.
Missing data are often imputed for analysis, but a single imputation may be inaccurate when performing feature selection in mining data. Feature selection procedures applied to multiply imputed data demonstrate this phenomenon and suggest that multiple imputation is an important adjunct to knowledge discovery.
Holmes
John H
JH
University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
Kamoun
Malek
M
Israni
Ajay
A
Joffe
Marshall
M
Yang
Wei
W
Feldman
Harold I
HI
D000465:Algorithms; D003628:Database Management Systems; D016208:Databases, Factual; D040901:Proteomics