Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery. (arXiv:2207.01822v1 [cs.LG])

Healthcare datasets present many challenges to both machine learning and
statistics as their data are typically heterogeneous, censored,
high-dimensional and have missing information. Feature selection is often used
to identify the important features but can produce unstable results when
applied to high-dimensional data, selecting a different set of features on each
iteration.

The stability of feature selection can be improved with the use of feature
selection ensembles, which aggregate the results of multiple base feature
selectors. A threshold must be …

alzheimer's arxiv data data-driven discovery disease ensemble feature feature selection lg thresholding