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- W4285675046 abstract "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." @default.
- W4285675046 created "2022-07-17" @default.
- W4285675046 creator A5037096829 @default.
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- W4285675046 date "2020-05-05" @default.
- W4285675046 modified "2023-09-26" @default.
- W4285675046 title "A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds" @default.
- W4285675046 doi "https://doi.org/10.48550/arxiv.2005.02249" @default.
- W4285675046 hasPublicationYear "2020" @default.
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