2 research outputs found
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds
A new robust algorithm based of 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
An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data
One of the most popular methods of the machine learning prediction
explanation is the SHapley Additive exPlanations method (SHAP). An imprecise
SHAP as a modification of the original SHAP is proposed for cases when the
class probability distributions are imprecise and represented by sets of
distributions. The first idea behind the imprecise SHAP is a new approach for
computing the marginal contribution of a feature, which fulfils the important
efficiency property of Shapley values. The second idea is an attempt to
consider a general approach to calculating and reducing interval-valued Shapley
values, which is similar to the idea of reachable probability intervals in the
imprecise probability theory. A simple special implementation of the general
approach in the form of linear optimization problems is proposed, which is
based on using the Kolmogorov-Smirnov distance and imprecise contamination
models. Numerical examples with synthetic and real data illustrate the
imprecise SHAP