Deep hurdle networks for zero-inflated multi-target regression: Application to multiple species abundance estimation

Shufeng Kong, Junwen Bai, Jae Hee Lee, Di Chen, Andrew Allyn, Michelle Stuart, Malin Pinsky, Katherine Mills, Carla P. Gomes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A key problem in computational sustainability is to understand the distribution of species across landscapes over time. This question gives rise to challenging large-scale prediction problems since (i) hundreds of species have to be simultaneously modeled and (ii) the survey data are usually inflated with zeros due to the absence of species for a large number of sites. The problem of tackling both issues simultaneously, which we refer to as the zero-inflated multi-target regression problem, has not been addressed by previous methods in statistics and machine learning. In this paper, we propose a novel deep model for the zero-inflated multi-target regression problem. To this end, we first model the joint distribution of multiple response variables as a multivariate probit model and then couple the positive outcomes with a multivariate log-normal distribution. By penalizing the difference between the two distributions' covariance matrices, a link between both distributions is established. The whole model is cast as an end-to-end learning framework and we provide an efficient learning algorithm for our model that can be fully implemented on GPUs. We show that our model outperforms the existing state-of-the-art baselines on two challenging real-world species distribution datasets concerning bird and fish populations.

Original languageEnglish (US)
Title of host publicationProceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020
EditorsChristian Bessiere
PublisherInternational Joint Conferences on Artificial Intelligence
Pages4375-4381
Number of pages7
ISBN (Electronic)9780999241165
StatePublished - 2020
Event29th International Joint Conference on Artificial Intelligence, IJCAI 2020 - Yokohama, Japan
Duration: Jan 1 2021 → …

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2021-January

Conference

Conference29th International Joint Conference on Artificial Intelligence, IJCAI 2020
Country/TerritoryJapan
CityYokohama
Period1/1/21 → …

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Deep hurdle networks for zero-inflated multi-target regression: Application to multiple species abundance estimation'. Together they form a unique fingerprint.

Cite this