Incorporating Boosted Regression Trees into Ecological Latent Variable Models

Important ecological phenomena are often observed indirectly. Consequently, probabilistic latent variable models provide an important tool, because they can include explicit models of the ecological phenomenon of interest and the process by which it is observed. However, existing latent variable methods rely on handformulated parametric models, which are expensive to design and require extensive preprocessing of the data. Nonparametric methods (such as regression trees) automate these decisions and produce highly accurate models. However, existing tree methods learn direct mappings from inputs to outputs—they cannot be applied to latent variable models. This paper describes a methodology for integrating nonparametric tree methods into probabilistic latent variable models by extending functional gradient boosting. The approach is presented in the context of occupancydetection (OD) modeling, where the goal is to model the distribution of a species from imperfect detections. Experiment...

• Type: Journal
• Year: 2011
• Where: AAAI
• Authors: Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich
• Keywords: AAAI 2011, Intelligent Agents, Latent Variable Models, Logistic Regression Models, Od Models