Spatiotemporal Relational Random Forests

We introduce and validate Spatiotemporal Relational Random Forests, which are random forests created with spatiotemporal relational probability trees. We build on the documented success of random forests by bringing spatiotemporal capabilities to the trees, enabling them to identify critical spatial, temporal, and spatiotemporal features in the data. We validate our results on simulated data and real-world convectively-induced turbulence data from a commercial airline flying in the continental United States.

[1]  Mark R. Segal,et al.  Machine Learning Benchmarks and Random Forest Regression , 2004 .

[2]  Jennifer Neville,et al.  Learning relational probability trees , 2003, KDD '03.

[3]  Andrew Cotter,et al.  A HYBRID MACHINE LEARNING AND FUZZY LOGIC APPROACH TO CIT DIAGNOSTIC DEVELOPMENT , 2006 .

[4]  Paul R. Cohen,et al.  Multiple Comparisons in Induction Algorithms , 2000, Machine Learning.

[5]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[6]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[7]  John K. Williams,et al.  NEXRAD Detection of Hazardous Turbulence , 2006 .

[8]  Robert C. Holte,et al.  Decision Tree Instability and Active Learning , 2007, ECML.

[9]  Luc Devroye,et al.  Consistency of Random Forests and Other Averaging Classifiers , 2008, J. Mach. Learn. Res..

[10]  Jennifer Neville,et al.  Exploiting time-varying relationships in statistical relational models , 2007, WebKDD/SNA-KDD '07.

[11]  Bruce Carmichael,et al.  VARIED RESEARCH EFFORTS ARE UNDER WAY TO FIND MEANS OF AVOIDING AIR TURBULENCE. , 1993 .

[12]  R. Frehlich,et al.  Estimates of Turbulence from Numerical Weather Prediction Model Output with Applications to Turbulence Diagnosis and Data Assimilation , 2004 .

[13]  M. Pal,et al.  Random forests for land cover classification , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[14]  Robert Sharman,et al.  Some Influences of Background Flow Conditions on the Generation of Turbulence due to Gravity Wave Breaking above Deep Convection , 2008 .

[15]  John K. Williams,et al.  Combining observations and model data for short-term storm forecasting , 2008, Optical Engineering + Applications.

[16]  C. A. Glasbey,et al.  Modelling weather data , 2003 .

[17]  Robert Sharman,et al.  An Investigation of Turbulence Generation Mechanisms above Deep Convection , 2003 .

[18]  Peter Talkner,et al.  Some remarks on spatial correlation function models , 1993 .

[19]  Amy McGovern,et al.  Spatiotemporal Relational Probability Trees: An Introduction , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[20]  Robert Sharman,et al.  An Integrated Approach to Mid- and Upper-Level Turbulence Forecasting , 2006 .

[21]  John K. Williams,et al.  Remote detection and diagnosis of thunderstorm turbulence , 2008, Optical Engineering + Applications.

[22]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[23]  Robert G. Fovell,et al.  A case study of convectively-induced clear air turbulence , 2007 .

[24]  Jennifer Neville,et al.  Temporal-Relational Classifiers for Prediction in Evolving Domains , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[25]  Andrew Zisserman,et al.  Image Classification using Random Forests and Ferns , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[26]  Jiawei Han,et al.  Geographic Data Mining and Knowledge Discovery , 2001 .

[27]  Nicolai Meinshausen,et al.  Quantile Regression Forests , 2006, J. Mach. Learn. Res..

[28]  Pedro A. Valdes-Sosa,et al.  Spatio-temporal autoregressive models defined over brain manifolds , 2007, Neuroinformatics.