A new method called SurvLIME for explaining machine learning survival models is proposed. It can be viewed as an extension or modification of the well-known method LIME. The main idea behind the proposed method is to apply the Cox proportional hazards model to approximate the survival model at the local area around a test example. The Cox model is used because it considers a linear combination of the example covariates such that coefficients of the covariates can be regarded as quantitative impacts on the prediction. Another idea is to approximate cumulative hazard functions of the explained model and the Cox model by using a set of perturbed points in a local area around the point of interest. The method is reduced to solving an unconstrained convex optimization problem. A lot of numerical experiments demonstrate the SurvLIME efficiency.

提出了一种称为SurvLIME的新方法来解释机器学习生存模型。它可以看作是众所周知的LIME方法的扩展或修改。所提出方法背后的主要思想是应用Cox比例风险模型来近似测试示例周围局部区域的生存模型。使用Cox模型是因为它考虑了示例协变量的线性组合，因此协变量的系数可被视为对预测的定量影响。另一个想法是通过在感兴趣点周围的局部区域中使用一组扰动点来近似说明模型和Cox模型的累积危害函数。该方法简化为解决无约束凸优化问题。许多数值实验证明了SurvLIME的效率。