Active Learning with Support Vector Machines for Tornado Prediction

Authors:
Theodore B. Trafalis;Indra Adrianto;Michael B. Richman
Affiliations:
School of Industrial Engineering, University of Oklahoma, 202 West Boyd St, Room 124, Norman, OK 73019, USA;School of Industrial Engineering, University of Oklahoma, 202 West Boyd St, Room 124, Norman, OK 73019, USA;School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd, Suite 5900, Norman, OK 73072, USA
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Year:
2007

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Abstract

In this paper, active learning with support vector machines (SVMs) is applied to the problem of tornado prediction. This method is used to predict which storm-scale circulations yield tornadoes based on the radar derived Mesocyclone Detection Algorithm (MDA) and near-storm environment (NSE) attributes. The main goal of active learning is to choose the instances or data points that are important or have influence to our model to be labeled and included in the training set. We compare this method to passive learning with SVMs where the next instances to be included to the training set are randomly selected. The preliminary results show that active learning can achieve high performance and significantly reduce the size of training set.