A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds
This work addresses the need for robust explanations in survival analysis, particularly for unreliable models, but it is incremental as it builds on the existing SurvLIME method.
The authors tackled the problem of explaining unreliable machine learning survival models by proposing SurvLIME-KS, a robust algorithm that ensures reliability with small training data or outliers, and demonstrated its efficiency through numerical experiments on synthetic and real datasets.
A new robust algorithm based of the explanation method SurvLIME called SurvLIME-KS is proposed for explaining machine learning survival models. The algorithm is developed to ensure robustness to cases of a small amount of training data or outliers of survival data. The first idea behind SurvLIME-KS is to apply the Cox proportional hazards model to approximate the black-box survival model at the local area around a test example due to the linear relationship of covariates in the model. The second idea is to incorporate the well-known Kolmogorov-Smirnov bounds for constructing sets of predicted cumulative hazard functions. As a result, the robust maximin strategy is used, which aims to minimize the average distance between cumulative hazard functions of the explained black-box model and of the approximating Cox model, and to maximize the distance over all cumulative hazard functions in the interval produced by the Kolmogorov-Smirnov bounds. The maximin optimization problem is reduced to the quadratic program. Various numerical experiments with synthetic and real datasets demonstrate the SurvLIME-KS efficiency.