SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models
This is an incremental improvement for researchers and practitioners needing interpretable survival models, particularly in data-scarce scenarios.
The paper tackles the problem of explaining machine learning survival models by proposing SurvLIME-Inf, a modification of SurvLIME that uses L∞-norm for distances, simplifying it to a linear programming problem and improving performance with small training sets, as demonstrated in numerical experiments.
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.