SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the
  Survival Models
Lev V. Utkin Danila Y. Eremenko Andrei V. Konstantinov
A new method called the Survival Beran-based Neural Importance Model
(SurvBeNIM) is proposed. It aims to explain predictions of machine learning
survival models, which are in the form of survival or cumulative hazard
functions. The main idea behind SurvBeNIM is to extend the Beran estimator by
incorporating the importance functions into its kernels and by implementing
these importance functions as a set of neural networks which are jointly
trained in an end-to-end manner. Two strategies of using and training the whole
neural network implementing SurvBeNIM are proposed. The first one explains a
single instance, and the neural network is trained for each explained instance.
According to the second strategy, the neural network only learns once on all
instances from the dataset and on all generated instances. Then the neural
network is used to explain any instance in a dataset domain. Various numerical
experiments compare the method with different existing explanation methods. A
code implementing the proposed method is publicly available.