  Understanding how environmental characteristics affect bio-diversity
patterns, from individual species to communities of species, is critical for
mitigating effects of global change. A central goal for conservation planning
and monitoring is the ability to accurately predict the occurrence of species
communities and how these communities change over space and time. This in turn
leads to a challenging and long-standing problem in the field of computer
science - how to perform ac-curate multi-label classification with hundreds of
labels? The key challenge of this problem is its exponential-sized output space
with regards to the number of labels to be predicted.Therefore, it is essential
to facilitate the learning process by exploiting correlations (or dependency)
among labels. Previous methods mostly focus on modelling the correlation on
label pairs; however, complex relations between real-world objects often go
beyond second order. In this paper, we pro-pose a novel framework for
multi-label classification, High-order Tie-in Variational Autoencoder
(HOT-VAE), which per-forms adaptive high-order label correlation learning. We
experimentally verify that our model outperforms the existing state-of-the-art
approaches on a bird distribution dataset on both conventional F1 scores and a
variety of ecological metrics. To show our method is general, we also perform
empirical analysis on seven other public real-world datasets in several
application domains, and Hot-VAE exhibits superior performance to previous
methods.
