  This supplementary material aims to describe the proposed multi-label
classification (MLC) search spaces based on the MEKA and WEKA softwares. First,
we overview 26 MLC algorithms and meta-algorithms in MEKA, presenting their
main characteristics, such as hyper-parameters, dependencies and constraints.
Second, we review 28 single-label classification (SLC) algorithms,
preprocessing algorithms and meta-algorithms in the WEKA software. These SLC
algorithms were also studied because they are part of the proposed MLC search
spaces. Fundamentally, this occurs due to the problem transformation nature of
several MLC algorithms used in this work. These algorithms transform an MLC
problem into one or several SLC problems in the first place and solve them with
SLC model(s) in a next step. Therefore, understanding their main
characteristics is crucial to this work. Finally, we present a formal
description of the search spaces by proposing a context-free grammar that
encompasses the 54 learning algorithms. This grammar basically comprehends the
possible combinations, the constraints and dependencies among the learning
algorithms.
