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

arXiv: https://arxiv.org/abs/2005.02387 · HTML: https://mlsanity.com/paper/2005.02387/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2020.

## Built on by (most cited first, 7 of 7)

- [Which LIME should I trust? Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2503.24365/) (2025)
- [SurvLIMEpy: A Python package implementing SurvLIME](https://mlsanity.com/paper/2302.10571/) (2023)
- [Interpretable Machine Learning for Survival Analysis](https://mlsanity.com/paper/2403.10250/) (2024)
- [Explainable AI for survival analysis: a median-SHAP approach](https://mlsanity.com/paper/2402.00072/) (2024)
- [L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy na"ive breast cancer patients](https://mlsanity.com/paper/2108.10365/) (2021)
- [Area-norm COBRA on Conditional Survival Prediction](https://mlsanity.com/paper/2309.00417/) (2023)
- [Identifying and Characterising Response in Clinical Trials: Development and Validation of a Machine Learning Approach in Colorectal Cancer](https://mlsanity.com/paper/2603.00757/) (2026)

## Abstract (arXiv)

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∞ -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.