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BEST: a decision tree algorithm that handles missing values

Author

Listed:
  • Cédric Beaulac

    (University of Toronto)

  • Jeffrey S. Rosenthal

    (University of Toronto)

Abstract

The main contribution of this paper is the development of a new decision tree algorithm. The proposed approach allows users to guide the algorithm through the data partitioning process. We believe this feature has many applications but in this paper we demonstrate how to utilize this algorithm to analyse data sets containing missing values. We tested our algorithm against simulated data sets with various missing data structures and a real data set. The results demonstrate that this new classification procedure efficiently handles missing values and produces results that are slightly more accurate and more interpretable than most common procedures without any imputations or pre-processing.

Suggested Citation

  • Cédric Beaulac & Jeffrey S. Rosenthal, 2020. "BEST: a decision tree algorithm that handles missing values," Computational Statistics, Springer, vol. 35(3), pages 1001-1026, September.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:3:d:10.1007_s00180-020-00987-z
    DOI: 10.1007/s00180-020-00987-z
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
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