  Recently, deep neural networks have expanded the state-of-art in various
scientific fields and provided solutions to long standing problems across
multiple application domains. Nevertheless, they also suffer from weaknesses
since their optimal performance depends on massive amounts of training data and
the tuning of an extended number of parameters. As a countermeasure, some
deep-forest methods have been recently proposed, as efficient and low-scale
solutions. Despite that, these approaches simply employ label classification
probabilities as induced features and primarily focus on traditional
classification and regression tasks, leaving multi-output prediction
under-explored. Moreover, recent work has demonstrated that tree-embeddings are
highly representative, especially in structured output prediction. In this
direction, we propose a novel deep tree-ensemble (DTE) model, where every layer
enriches the original feature set with a representation learning component
based on tree-embeddings. In this paper, we specifically focus on two
structured output prediction tasks, namely multi-label classification and
multi-target regression. We conducted experiments using multiple benchmark
datasets and the obtained results confirm that our method provides superior
results to state-of-the-art methods in both tasks.
