  New proposals in the field of multi-label learning algorithms have been
growing in number steadily over the last few years. The experimentation
associated with each of them always goes through the same phases: selection of
datasets, partitioning, training, analysis of results and, finally, comparison
with existing methods. This last step is often hampered since it involves using
exactly the same datasets, partitioned in the same way and using the same
validation strategy. In this paper we present a set of tools whose objective is
to facilitate the management of multi-label datasets, aiming to standardize the
experimentation procedure. The two main tools are an R package, mldr.datasets,
and a web repository with datasets, Cometa. Together, these tools will simplify
the collection of datasets, their partitioning, documentation and export to
multiple formats, among other functions. Some tips, recommendations and
guidelines for a good experimental analysis of multi-label methods are also
presented.
