论文标题
survlime-inf：简化的生存修改，用于解释机器学习生存模型
SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models
论文作者
论文摘要
提出了一种解释机器学习生存模型的称为Survlime-Inf的解释方法的新修改。生存背后的基本思想以及Survlime-Inf是应用COX比例危害模型，以近似测试示例的局部区域的黑盒生存模型。 COX模型由于协变量的线性关系而使用。与生存相反，提出的修改使用$ l _ {\ infty} $ - 定义近似和近似累积危害功能之间的距离。这导致了一个简单的线性编程问题，用于确定重要功能和解释黑框模型预测。此外，当训练组很小时，Survlime-Inf的表现要优先于生存。合成和实际数据集的数值实验证明了Survlime-Inf效率。
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.
