  A feature selection algorithm should ideally satisfy four conditions:
reliably extract relevant features; be able to identify non-linear feature
interactions; scale linearly with the number of features and dimensions; allow
the incorporation of known sparsity structure. In this work we propose a novel
feature selection algorithm, Gradient Boosted Feature Selection (GBFS), which
satisfies all four of these requirements. The algorithm is flexible, scalable,
and surprisingly straight-forward to implement as it is based on a modification
of Gradient Boosted Trees. We evaluate GBFS on several real world data sets and
show that it matches or out-performs other state of the art feature selection
algorithms. Yet it scales to larger data set sizes and naturally allows for
domain-specific side information.
