SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models

Abstract

A new modification of the explanation method SurvLIME called SurvLIME-Inf for explaining machine learning survival models is proposed. The basic idea behind SurvLIME as well as SurvLIME-Inf is to apply the Cox proportional hazards model to approximate the black-box survival model at the local area around a test example. The Cox model is used due to the linear relationship of covariates. In contrast to SurvLIME, the proposed modification uses $L_{\infty }$-norm for defining distances between approximating and approximated cumulative hazard functions. This leads to a simple linear programming problem for determining important features and for explaining the black-box model prediction. Moreover, SurvLIME-Inf outperforms SurvLIME when the training set is very small. Numerical experiments with synthetic and real datasets demonstrate the SurvLIME-Inf efficiency.

arXiv admin note: substantial text overlap with arXiv:2003.08371, arXiv:2005.02249

References in corpus (5)

• What do we need to build explainable AI systems for the medical domain?
• Machine Learning for Survival Analysis: A Survey
• Explaining the Explainer: A First Theoretical Analysis of LIME
• NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks
• A Modified Perturbed Sampling Method for Local Interpretable Model-agnostic Explanation