  In supervised learning, simple baseline classifiers can be constructed by
only looking at the class, i.e., ignoring any other information from the
dataset. The single-label learning community frequently uses as a reference the
one which always predicts the majority class. Although a classifier might
perform worse than this simple baseline classifier, this behaviour requires a
special explanation. Aiming to motivate the community to compare experimental
results with the ones provided by a multi-label baseline classifier, calling
the attention about the need of special explanations related to classifiers
which perform worse than the baseline, in this work we propose the use of
General_B, a multi-label baseline classifier. General_B was evaluated in
contrast to results published in the literature which were carefully selected
using a systematic review process. It was found that a considerable number of
published results on 10 frequently used datasets are worse than or equal to the
ones obtained by General_B, and for one dataset it reaches up to 43% of the
dataset published results. Moreover, although a simple baseline classifier was
not considered in these publications, it was observed that even for very poor
results no special explanations were provided in most of them. We hope that the
findings of this work would encourage the multi-label community to consider the
idea of using a simple baseline classifier, such that further explanations are
provided when a classifiers performs worse than a baseline.
