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A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds.

Maxim S Kovalev1, Lev V Utkin1

  • 1Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia.

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|August 26, 2020
PubMed
Summary

A new algorithm, SurvLIME-KS, enhances machine learning survival model explanations. It ensures robustness with limited data or outliers, improving reliability for complex survival data analysis.

Keywords:
Censored dataExplainable AIInterpretable modelKolmogorov–Smirnov boundsSurvival analysisThe Cox model

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Area of Science:

  • Machine Learning
  • Survival Analysis
  • Explainable AI

Background:

  • Machine learning survival models are crucial for predicting event times but often lack transparency.
  • Existing explanation methods may struggle with small datasets or data outliers, limiting their reliability.
  • Robustness in explaining survival models is essential for trustworthy predictions in critical applications.

Purpose of the Study:

  • To introduce SurvLIME-KS, a novel and robust algorithm for explaining machine learning survival models.
  • To enhance the reliability of survival model explanations, particularly in scenarios with limited training data or the presence of outliers.
  • To provide a more trustworthy and interpretable approach to complex survival data analysis.

Main Methods:

  • The SurvLIME-KS algorithm leverages the Cox proportional hazards model for local approximation of black-box survival models.
  • It incorporates Kolmogorov-Smirnov bounds to construct sets of predicted cumulative hazard functions.
  • A robust maximin strategy is employed, optimizing the distance between model and approximation within KS bounds, reduced to a quadratic program.

Main Results:

  • SurvLIME-KS demonstrates robustness against small training data and outliers in survival data.
  • Numerical experiments on synthetic and real datasets validate the efficiency of the proposed algorithm.
  • The method effectively explains complex survival models, providing reliable insights.

Conclusions:

  • SurvLIME-KS offers a significant advancement in the explainability of machine learning survival models.
  • The algorithm's robustness makes it suitable for real-world applications with imperfect data.
  • This work contributes to more reliable and interpretable survival data analysis using AI.