A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds.

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ID: 110429
2020
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Abstract
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.
Reference Key
kovalev2020aneural Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kovalev, Maxim S;Utkin, Lev V;
Journal neural networks : the official journal of the international neural network society
Year 2020
DOI
S0893-6080(20)30296-3
URL
Keywords
survival analysis explainable ai interpretable model censored data kolmogorov–smirnov bounds the cox model

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