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Statistics > Machine Learning

arXiv:2309.03561 (stat)
[Submitted on 7 Sep 2023]

Title:Trinary Decision Trees for missing value handling

Authors:Henning Zakrisson
Download a PDF of the paper titled Trinary Decision Trees for missing
value handling, by Henning Zakrisson
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    Abstract: This paper introduces the Trinary decision tree, an
    algorithm designed to improve the handling of missing data in
    decision tree regressors and classifiers. Unlike other
    approaches, the Trinary decision tree does not assume that
    missing values contain any information about the response. Both
    theoretical calculations on estimator bias and numerical
    illustrations using real data sets are presented to compare its
    performance with established algorithms in different missing data
    scenarios (Missing Completely at Random (MCAR), and Informative
    Missingness (IM)). Notably, the Trinary tree outperforms its
    peers in MCAR settings, especially when data is only missing
    out-of-sample, while lacking behind in IM settings. A hybrid
    model, the TrinaryMIA tree, which combines the Trinary tree and
    the Missing In Attributes (MIA) approach, shows robust
    performance in all types of missingness. Despite the potential
    drawback of slower training speed, the Trinary tree offers a
    promising and more accurate method of handling missing data in
    decision tree algorithms.

Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG)
Cite as:  arXiv:2309.03561 [stat.ML]
          (or arXiv:2309.03561v1 [stat.ML] for this version)
          https://doi.org/10.48550/arXiv.2309.03561
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Henning Zakrisson [view email]
[v1] Thu, 7 Sep 2023 08:44:25 UTC (13 KB)
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