= \{{\lambda}_1 ,  {\lambda}_2 , ... ,  {\lambda}_q \}$ is the set of all labels. Finally, the Macro measure will be simply the harmonic mean as calculated in equation of F1-score using these two averages $p$ and $r$.

As a third evaluation metric, we use *Mean Ranking*. As we have a potential large number of results, considering all base methods and datasets, it is important to have a global measure of performance for all these combinations in a single metric. For doing so, we ranked the methods for each dataset. The Mean Ranking is the simple sum of ranks obtained by a method in each dataset divided by the total number of datasets. It is important to notice that the rank was calculated based on Macro F1 because of the high imbalance among the classes in several datasets.

0 , i.e.:

$$MeanRank(m) = \frac{ \sum_{i=1}^D r_{i} } { |D| }$$

in which $D$ is the set of datasets and $r_i$ is the rank of the method $m$ for dataset $i$.

Finally, our **experiments** were run using a 5-fold cross validation setup, with best parameters for the learning methods found using cross-validation within the training set. This procedure was applied to 