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Robust Decision Tree

Et Robust Decision Tree er en variant af beslutningstræer, der er trænet med modificerede opdelingskriterier eller træningsprocedurer designet til at reducere følsomheden over for outliers, labelstøj og adversariale forstyrrelser. I stedet for at minimere standard urenhedsmål, der er stærkt påvirket af ekstreme værdier, anvender robuste varianter statistisk robuste analoger eller regularisering til at producere opdelinger, der generaliserer under støjfyldte eller korrupte dataforhold.

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Kilder

  1. Chen, H., & Nan, F. (2019). Robust Decision Trees Against Adversarial Examples. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, 1006–1015. link
  2. Hubert, M., & Debruyne, M. (2010). Minimum covariance determinant. Wiley Interdisciplinary Reviews: Computational Statistics, 2(1), 36–43. (background on robust estimation applied to tree splitting criteria) DOI: 10.1002/wics.61

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ScholarGate. (2026, June 3). Robust Decision Tree (Outlier-Resistant Tree Induction). ScholarGate. https://scholargate.app/da/machine-learning/robust-decision-tree

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ScholarGateRobust Decision Tree (Robust Decision Tree (Outlier-Resistant Tree Induction)). Hentet 2026-06-15 fra https://scholargate.app/da/machine-learning/robust-decision-tree · Datasæt: https://doi.org/10.5281/zenodo.20539026