Flat random forest: a new ensemble learning method towards better training efficiency and adaptive model size to deep forest

The known deficiencies of deep neural networks include inferior training efficiency, weak parallelization capability, too many hyper-parameters etc. To address these issues, some researchers presented deep forest, a special deep learning model, which achieves some significant improvements but remain poor training efficiency, inflexible model size and weak interpretability. This paper endeavors to solve the issues in a new way. Firstly, deep forest is extended to the densely connected deep forest to enhance the prediction accuracy. Secondly, to perform parallel training with adaptive model size, the flat random forest is proposed by achieving the balance between the width and depth of densely connected deep forest. Finally, two core algorithms are respectively presented for the forward output weights computation and output weights updating. The experimental results show, compared with deep forest, the proposed flat random forest acquires competitive prediction accuracy, higher training efficiency, less hyper-parameters and adaptive model size.