  In this work, we propose two novel (groups of) methods for unsupervised
feature ranking and selection. The first group includes feature ranking scores
(Genie3 score, RandomForest score) that are computed from ensembles of
predictive clustering trees. The second method is URelief, the unsupervised
extension of the Relief family of feature ranking algorithms. Using 26
benchmark data sets and 5 baselines, we show that both the Genie3 score
(computed from the ensemble of extra trees) and the URelief method outperform
the existing methods and that Genie3 performs best overall, in terms of
predictive power of the top-ranked features. Additionally, we analyze the
influence of the hyper-parameters of the proposed methods on their performance,
and show that for the Genie3 score the highest quality is achieved by the most
efficient parameter configuration. Finally, we propose a way of discovering the
location of the features in the ranking, which are the most relevant in
reality.
