Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images

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Single Subject Prediction of Brain Disorders in Neuroimaging: Promises and Pitfalls (2016, background): “…AD = 96, Total = 350 MCI-Conv: 67.1% Cui et al. (2011) t2:83 sMRI AD/MCI Hippocampus and parahippocampal gyrus GM maps 11,031 SVM HC = 188, MCI = 260, AD = 131 70–85% MCI-Conv: 65% Chu et al. (2012) t2:84 sMRI AD/MCI Intensity and texture of selected VOIs in MTL N100,000 Ensemble of SVMs HC =…”
The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features (2018, unknown): “635 and larger sample sizes can increase classification accuracy (Chu et al., 2012; Kl€ oppel et al., 2008).”
PRoNTo: Pattern Recognition for Neuroimaging Toolbox (2013, background): “However, some authors have questioned the benefits of feature selection in neuroimaging applications (Cuingnet et al. 2011; Chu et al., 2012).”