Enhanced spatiotemporal relational probability trees and forests

Authors:
Amy Mcgovern;Nathaniel Troutman;Rodger A. Brown;John K. Williams;Jennifer Abernethy
Affiliations:
School of Computer Science, University of Oklahoma, Norman, USA 73019;School of Computer Science, University of Oklahoma, Norman, USA 73019;NOAA/National Severe Storms Laboratory, Norman, USA 73072;Research Applications Laboratory, National Center for Atmospheric Research, Boulder, USA 80307;Research Applications Laboratory, National Center for Atmospheric Research, Boulder, USA 80307
Venue:
Data Mining and Knowledge Discovery
Year:
2013

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Abstract

Many real world domains are inherently spatiotemporal in nature. In this work, we introduce significant enhancements to two spatiotemporal relational learning methods, the spatiotemporal relational probability tree and the spatiotemporal relational random forest, that increase their ability to learn using spatiotemporal data. We enabled the models to formulate questions on both objects and the scalar and vector fields within and around objects, allowing the models to differentiate based on the gradient, divergence, and curl and to recognize the shape of point clouds defined by fields. This enables the model to ask questions about the change of a shape over time or about its orientation. These additions are validated on several real-world hazardous weather datasets. We demonstrate that these additions enable the models to learn robust classifiers that outperform the versions without these new additions. In addition, analysis of the learned models shows that the findings are consistent with current meteorological theories.