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Machine-learning classifiers for imbalanced tornado data

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  • Theodore Trafalis
  • Indra Adrianto
  • Michael Richman
  • S. Lakshmivarahan

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. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Theodore Trafalis & Indra Adrianto & Michael Richman & S. Lakshmivarahan, 2014. "Machine-learning classifiers for imbalanced tornado data," Computational Management Science, Springer, vol. 11(4), pages 403-418, October.
  • Handle: RePEc:spr:comgts:v:11:y:2014:i:4:p:403-418
    DOI: 10.1007/s10287-013-0174-6
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    References listed on IDEAS

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    1. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, April.
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    1. Elaheh Jafarigol & Theodore B. Trafalis, 2024. "A distributed approach to meteorological predictions: addressing data imbalance in precipitation prediction models through federated learning and GANs," Computational Management Science, Springer, vol. 21(1), pages 1-23, June.
    2. Wenjuan Sun & Paolo Bocchini & Brian D. Davison, 2020. "Applications of artificial intelligence for disaster management," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 2631-2689, September.

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