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Robust Random Forest-Based All-Relevant Feature Ranks for Trustworthy AI.

Bastian Pfeifer1, Andreas Holzinger1,2, Michael G Schimek1

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This study enhances feature selection stability in machine learning using consensus values and rank aggregation. The proposed method improves accuracy and robustness for reliable biomarker discovery and trustworthy AI applications.

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Area of Science:

  • Machine Learning
  • Bioinformatics
  • Computational Statistics

Background:

  • Feature selection is crucial for machine learning, especially in bioinformatics for biomarker discovery.
  • Tree-based methods are commonly employed for feature selection.
  • Stochastic algorithms in feature selection can lead to unstable rankings.

Purpose of the Study:

  • To investigate the feature ranking instability of BORUTA, VITA, and regularized random forest (RRF) methods.
  • To propose a novel approach for stabilizing feature ranks using consensus values and rank aggregation.
  • To enhance the accuracy and robustness of feature selection for practical machine learning.

Main Methods:

  • Studied the stability of BORUTA, VITA, and regularized random forest (RRF) feature selection algorithms.
  • Investigated feature ranking instability in stochastic algorithms.
  • Applied rank aggregation techniques to compute consensus values from multiple feature selection runs.

Main Results:

  • Identified feature ranking instability in stochastic tree-based feature selection methods.
  • Demonstrated that consensus values from multiple runs stabilize feature ranks.
  • Consolidated features showed improved accuracy and robustness compared to single-run methods.

Conclusions:

  • Consensus-based feature selection significantly enhances stability and reliability.
  • The proposed method improves the trustworthiness of machine learning applications, particularly in biomarker discovery.
  • Rank aggregation techniques offer a robust solution for feature selection instability.