Deep tree-ensembles for multi-output prediction

作者：

Highlights：

• A state-of-the-art deep tree-ensemble method for multi-target regression and multi-label classification.

• Low-dimensional tree-embeddings are more representative than output features in deep-forests architectures.

• A stopping (pruning) criterion to determine the optimal number of layers as well as mechanisms to surpass overfitting.

• An extensive evaluation on 41 datasets, comparing our approach to state-of-the-art methods.

摘要

•A state-of-the-art deep tree-ensemble method for multi-target regression and multi-label classification.•Low-dimensional tree-embeddings are more representative than output features in deep-forests architectures.•A stopping (pruning) criterion to determine the optimal number of layers as well as mechanisms to surpass overfitting.•An extensive evaluation on 41 datasets, comparing our approach to state-of-the-art methods.

论文关键词：Ensemble learning,Deep-forest,Multi-output prediction,Multi-target regression,Multi-label classification

论文评审过程：Received 3 November 2020, Revised 13 July 2021, Accepted 27 July 2021, Available online 4 August 2021, Version of Record 20 August 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108211