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Related Experiment Video

Updated: May 12, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

An AUC-based permutation variable importance measure for random forests.

Silke Janitza1, Carolin Strobl, Anne-Laure Boulesteix

  • 1Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, D-81377, Munich, Germany. janitza@ibe.med.uni-muenchen.de

BMC Bioinformatics
|April 9, 2013
PubMed
Summary
This summary is machine-generated.

The standard permutation variable importance measure (VIM) in random forest models performs poorly with unbalanced data. An improved AUC-based VIM offers better performance for imbalanced datasets, maintaining similar results for balanced data.

Related Experiment Videos

Last Updated: May 12, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Machine Learning
  • Statistical Modeling
  • Bioinformatics

Background:

  • Random Forest (RF) is a popular classification and variable importance tool for high-dimensional data.
  • RF classification performance degrades with unbalanced data (unequal class sizes).
  • Variable importance measures (VIMs) in RF have not been thoroughly evaluated for unbalanced data.

Purpose of the Study:

  • To investigate the performance of the standard permutation VIM with unbalanced data.
  • To introduce and evaluate a novel AUC-based permutation VIM robust to class imbalance.

Main Methods:

  • Explored standard and AUC-based permutation VIMs using simulated and real-world imbalanced datasets.
  • Compared VIM performance across varying levels of class imbalance.

Main Results:

  • The novel AUC-based permutation VIM significantly outperforms the standard permutation VIM on unbalanced data.
  • Both VIMs demonstrate comparable performance on balanced data.
  • Standard permutation VIM's discrimination ability decreases with increasing class imbalance.

Conclusions:

  • The AUC-based permutation VIM is a more robust measure for variable importance in the presence of class imbalance.
  • The new VIM is implemented in the R package 'party' for conditional inference trees.
  • Study codes are available for reproducibility.