A new robust algorithm based on the explanation method SurvLIME called SurvLIME-KS is proposed for explaining machine learning survival models. The algorithm is developed to ensure robustness to cases of a small amount of training data or outliers of survival data. The first idea behind SurvLIME-KS is to apply the Cox proportional hazards model to approximate the black-box survival model at the local area around a test example due to the linear relationship of covariates in the model. The second idea is to incorporate the well-known Kolmogorov–Smirnov bounds for constructing sets of predicted cumulative hazard functions. As a result, the robust maximin strategy is used, which aims to minimize the average distance between cumulative hazard functions of the explained black-box model and of the approximating Cox model, and to maximize the distance over all cumulative hazard functions in the interval produced by the Kolmogorov–Smirnov bounds. The maximin optimization problem is reduced to the quadratic program. Various numerical experiments with synthetic and real datasets demonstrate the SurvLIME-KS efficiency.

提出了一种基于解释方法SurvLIME的鲁棒算法，称为SurvLIME-KS，用于解释机器学习生存模型。开发该算法以确保对少量训练数据或生存数据异常值的情况具有鲁棒性。SurvLIME-KS背后的第一个想法是，由于模型中协变量之间的线性关系，将Cox比例风险模型应用于测试示例周围局部区域的黑匣子生存模型。第二个想法是将众所周知的Kolmogorov-Smirnov边界纳入到构建预测的累积危害函数集中。结果，使用了鲁棒的最大化策略，其目的是最小化所解释的黑盒模型和近似Cox模型的累积危害函数之间的平均距离，并在由Kolmogorov-Smirnov边界产生的区间内的所有累积危害函数上最大化距离。maximin最优化问题简化为二次规划。使用合成和真实数据集进行的各种数值实验证明了SurvLIME-KS的效率。