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Computational Management Science

, Volume 11, Issue 4, pp 403–418 | Cite as

Machine-learning classifiers for imbalanced tornado data

  • Theodore B. TrafalisEmail author
  • Indra Adrianto
  • Michael B. Richman
  • S. Lakshmivarahan
Original Paper

Abstract

Learning from imbalanced data, where the number of observations in one class is significantly larger than the ones in the other class, has gained considerable attention in the machine learning community. Assuming the difficulty in predicting each class is similar, most standard classifiers will tend to predict the majority class well. This study applies tornado data that are highly imbalanced, as they are rare events. The severe weather data used herein have thunderstorm circulations (mesocyclones) that produce tornadoes in approximately 6.7 % of the total number of observations. However, since tornadoes are high impact weather events, it is important to predict the minority class with high accuracy. In this study, we apply support vector machines (SVMs) and logistic regression with and without a midpoint threshold adjustment on the probabilistic outputs, random forest, and rotation forest for tornado prediction. Feature selection with SVM-recursive feature elimination was also performed to identify the most important features or variables for predicting tornadoes. The results showed that the threshold adjustment on SVMs provided better performance compared to other classifiers.

Keywords

Machine learning Support vector machines Random forest  Rotation forest Logistic regression Tornado detection 

Mathematics Subject Classification (2010)

62H30 68Q32 62J86 

Notes

Acknowledgments

Funding for this research was provided under the National Science Foundation Grants AGS0831359 and EIA-0205628.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Theodore B. Trafalis
    • 1
    Email author
  • Indra Adrianto
    • 1
  • Michael B. Richman
    • 2
  • S. Lakshmivarahan
    • 3
  1. 1.School of Industrial and Systems EngineeringThe University of OklahomaNormanUSA
  2. 2.School of MeteorologyThe University of OklahomaNormanUSA
  3. 3.School of Computer ScienceThe University of OklahomaNormanUSA

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