  Automated Machine Learning (AutoML) has emerged to deal with the selection
and configuration of algorithms for a given learning task. With the progression
of AutoML, several effective methods were introduced, especially for
traditional classification and regression problems. Apart from the AutoML
success, several issues remain open. One issue, in particular, is the lack of
ability of AutoML methods to deal with different types of data. Based on this
scenario, this paper approaches AutoML for multi-label classification (MLC)
problems. In MLC, each example can be simultaneously associated to several
class labels, unlike the standard classification task, where an example is
associated to just one class label. In this work, we provide a general
comparison of five automated multi-label classification methods -- two
evolutionary methods, one Bayesian optimization method, one random search and
one greedy search -- on 14 datasets and three designed search spaces. Overall,
we observe that the most prominent method is the one based on a canonical
grammar-based genetic programming (GGP) search method, namely
Auto-MEKA$_{GGP}$. Auto-MEKA$_{GGP}$ presented the best average results in our
comparison and was statistically better than all the other methods in different
search spaces and evaluated measures, except when compared to the greedy search
method.
