# Multi-Label Learning with Deep Forest

arXiv: https://arxiv.org/abs/1911.06557 · HTML: https://mlsanity.com/paper/1911.06557/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2019.

## Built on by (most cited first, 12 of 12)

- [On Explaining Random Forests with SAT](https://mlsanity.com/paper/2105.10278/) (2021)
- [Locally-Minimal Probabilistic Explanations](https://mlsanity.com/paper/2312.11831/) (2023)
- [Deep Forest with Hashing Screening and Window Screening](https://mlsanity.com/paper/2207.11951/) (2022)
- [HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides](https://mlsanity.com/paper/2111.06023/) (2021)
- [DBC-Forest: Deep forest with binning confidence screening](https://mlsanity.com/paper/2112.13182/) (2021)
- [A Characterization of Multioutput Learnability](https://mlsanity.com/paper/2301.02729/) (2023)
- [Interpreting Deep Forest through Feature Contribution and MDI Feature Importance](https://mlsanity.com/paper/2305.00805/) (2023)
- [Gated recurrent units and temporal convolutional network for multilabel classification](https://mlsanity.com/paper/2110.04414/) (2021)
- [The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forest](https://mlsanity.com/paper/2407.05108/) (2024)
- [Enhance Learning Efficiency of Oblique Decision Tree via Feature Concatenation](https://mlsanity.com/paper/2502.00465/) (2025)
- [Enhance and Reuse: A Dual-Mechanism Approach to Boost Deep Forest for Label Distribution Learning](https://mlsanity.com/paper/2602.06353/) (2026)
- [D2ACE: Multi-Label Batch Selection Guided by Dual Dynamics and Adaptive Correlation Enhancement](https://mlsanity.com/paper/2605.09400/) (2026)

## Abstract (arXiv)

In multi-label learning, each instance is associated with multiple labels and the crucial task is how to leverage label correlations in building models. Deep neural network methods usually jointly embed the feature and label information into a latent space to exploit label correlations. However, the success of these methods highly depends on the precise choice of model depth. Deep forest is a recent deep learning framework based on tree model ensembles, which does not rely on backpropagation. We consider the advantages of deep forest models are very appropriate for solving multi-label problems. Therefore we design the Multi-Label Deep Forest (MLDF) method with two mechanisms: measure-aware feature reuse and measure-aware layer growth. The measure-aware feature reuse mechanism reuses the good representation in the previous layer guided by confidence. The measure-aware layer growth mechanism ensures MLDF gradually increase the model complexity by performance measure. MLDF handles two challenging problems at the same time: one is restricting the model complexity to ease the overfitting issue; another is optimizing the performance measure on user's demand since there are many different measures in the multi-label evaluation. Experiments show that our proposal not only beats the compared methods over six measures on benchmark datasets but also enjoys label correlation discovery and other desired properties in multi-label learning.