  Several multi-target regression methods were devel-oped in the last years
aiming at improving predictive performanceby exploring inter-target correlation
within the problem. However, none of these methods outperforms the others for
all problems. This motivates the development of automatic approachesto
recommend the most suitable multi-target regression method. In this paper, we
propose a meta-learning system to recommend the best predictive method for a
given multi-target regression problem. We performed experiments with a
meta-dataset generated by a total of 648 synthetic datasets. These datasets
were created to explore distinct inter-targets characteristics toward
recommending the most promising method. In experiments, we evaluated four
different algorithms with different biases as meta-learners. Our meta-dataset
is composed of 58 meta-features, based on: statistical information, correlation
characteristics, linear landmarking, from the distribution and smoothness of
the data, and has four different meta-labels. Results showed that induced
meta-models were able to recommend the best methodfor different base level
datasets with a balanced accuracy superior to 70% using a Random Forest
meta-model, which statistically outperformed the meta-learning baselines.
