  Some data analysis applications comprise datasets, where explanatory
variables are expensive or tedious to acquire, but auxiliary data are readily
available and might help to construct an insightful training set. An example is
neuroimaging research on mental disorders, specifically learning a
diagnosis/prognosis model based on variables derived from expensive Magnetic
Resonance Imaging (MRI) scans, which often requires large sample sizes.
Auxiliary data, such as demographics, might help in selecting a smaller sample
that comprises the individuals with the most informative MRI scans. In active
learning literature, this problem has not yet been studied, despite promising
results in related problem settings that concern the selection of instances or
instance-feature pairs.
  Therefore, we formulate this complementary problem of Active Selection of
Classification Features (ASCF): Given a primary task, which requires to learn a
model f: x-> y to explain/predict the relationship between an
expensive-to-acquire set of variables x and a class label y. Then, the
ASCF-task is to use a set of readily available selection variables z to select
these instances, that will improve the primary task's performance most when
acquiring their expensive features z and including them to the primary training
set.
  We propose two utility-based approaches for this problem, and evaluate their
performance on three public real-world benchmark datasets. In addition, we
illustrate the use of these approaches to efficiently acquire MRI scans in the
context of neuroimaging research on mental disorders, based on a simulated
study design with real MRI data.
