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Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants [article]

Lara J. Kanbar, Charles C. Onu, Wissam Shalish, Karen A. Brown, Guilherme M. Sant'Anna, Robert E. Kearney, Doina Precup
2018 arXiv   pre-print

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https://web.archive.org/web/20181231184829/https://arxiv.org/pdf/1808.07992v1.pdf

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We address the issue of data imbalance by employing random undersampling of examples from the majority class before training each Decision Tree in a bag.  ...  We present an approach using Random Forest classifiers for the analysis of cardiorespiratory variability to predict extubation readiness.  ...  ACKNOWLEDGMENT We thank the patients and their families for their participation in this research.  ... 
arXiv:1808.07992v1 fatcat:sgcka7ywfndedcv5brybvheqe4
Citation

Lara J. Kanbar, Charles C. Onu, Wissam Shalish, Karen A. Brown, Guilherme M. Sant'Anna, Robert E. Kearney, Doina Precup. "Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants." arXiv (2018)