Spatiotemporal Relational Random Forests

被引：6

作者：

Supinie, Timothy A. ^{[1
]}

McGovern, Amy ^{[2
]}

Williams, John ^{[3
]}

Abernethy, Jennifer ^{[3
]}

机构：

[1] Univ Oklahoma, Sch Meteorol, Norman, OK 73072 USA

[2] Univ Oklahoma, Sch Comp Sci, Norman, OK 73019 USA

[3] Natl Ctr Atmospher Res, Res Applicat Lab, Boulder, CO 80301 USA

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2009) | 2009年

基金：

美国国家科学基金会;

关键词：

Spatiotemporal data mining; Relational learning; Random forests; Turbulence; TURBULENCE; GENERATION;

D O I：

10.1109/ICDMW.2009.89

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：630 / +

页数：2

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