  Healthcare datasets often contain groups of highly correlated features, such
as features from the same biological system. When feature selection is applied
to these datasets to identify the most important features, the biases inherent
in some multivariate feature selectors due to correlated features make it
difficult for these methods to distinguish between the important and irrelevant
features and the results of the feature selection process can be unstable.
Feature selection ensembles, which aggregate the results of multiple individual
base feature selectors, have been investigated as a means of stabilising
feature selection results, but do not address the problem of correlated
features. We present a novel framework to create feature selection ensembles
from multivariate feature selectors while taking into account the biases
produced by groups of correlated features, using agglomerative hierarchical
clustering in a pre-processing step. These methods were applied to two
real-world datasets from studies of Alzheimer's disease (AD), a progressive
neurodegenerative disease that has no cure and is not yet fully understood. Our
results show a marked improvement in the stability of features selected over
the models without clustering, and the features selected by these models are in
keeping with the findings in the AD literature.
