  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.
