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

• URL: http://arxiv.org/abs/2005.02387v1
• Date: Tue, 5 May 2020 14:34:46 GMT
• Title: SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models
• Authors: Lev V. Utkin, Maxim S. Kovalev and Ernest M. Kasimov
• Abstract summary: 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. In contrast to SurvLIME, the proposed modification uses $L_infty $-norm for defining distances between approximating and approximated cumulative hazard functions.
• Score: 4.640835690336653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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