  We study active feature selection, a novel feature selection setting in which
unlabeled data is available, but the budget for labels is limited, and the
examples to label can be actively selected by the algorithm. We focus on
feature selection using the classical mutual information criterion, which
selects the $k$ features with the largest mutual information with the label. In
the active feature selection setting, the goal is to use significantly fewer
labels than the data set size and still find $k$ features whose mutual
information with the label based on the \emph{entire} data set is large. We
explain and experimentally study the choices that we make in the algorithm, and
show that they lead to a successful algorithm, compared to other more naive
approaches. Our design draws on insights which relate the problem of active
feature selection to the study of pure-exploration multi-armed bandits
settings. While we focus here on mutual information, our general methodology
can be adapted to other feature-quality measures as well. The code is available
at the following url: https://github.com/ShacharSchnapp/ActiveFeatureSelection.
