  Recently, feature selection has become an increasingly important area of
research due to the surge in high-dimensional datasets in all areas of modern
life. A plethora of feature selection algorithms have been proposed, but it is
difficult to truly analyse the quality of a given algorithm. Ideally, an
algorithm would be evaluated by measuring how well it removes known bad
features. Acquiring datasets with such features is inherently difficult, and so
a common technique is to add synthetic bad features to an existing dataset.
While adding noisy features is an easy task, it is very difficult to
automatically add complex, redundant features. This work proposes one of the
first approaches to generating redundant features, using a novel genetic
programming approach. Initial experiments show that our proposed method can
automatically create difficult, redundant features which have the potential to
be used for creating high-quality feature selection benchmark datasets.
