  The data made available for analysis are becoming more and more complex along
several directions: high dimensionality, number of examples and the amount of
labels per example. This poses a variety of challenges for the existing machine
learning methods: coping with dataset with a large number of examples that are
described in a high-dimensional space and not all examples have labels
provided. For example, when investigating the toxicity of chemical compounds
there are a lot of compounds available, that can be described with information
rich high-dimensional representations, but not all of the compounds have
information on their toxicity. To address these challenges, we propose
semi-supervised learning of feature ranking. The feature rankings are learned
in the context of classification and regression as well as in the context of
structured output prediction (multi-label classification, hierarchical
multi-label classification and multi-target regression). To the best of our
knowledge, this is the first work that treats the task of feature ranking
within the semi-supervised structured output prediction context. More
specifically, we propose two approaches that are based on tree ensembles and
the Relief family of algorithms. The extensive evaluation across 38 benchmark
datasets reveals the following: Random Forests perform the best for the
classification-like tasks, while for the regression-like tasks Extra-PCTs
perform the best, Random Forests are the most efficient method considering
induction times across all tasks, and semi-supervised feature rankings
outperform their supervised counterpart across a majority of the datasets from
the different tasks.
