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SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models

arXiv 2312.06638 ↗First posted 11 Dec 2023cs.LG cs.AI stat.MLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 43 of its 78 references that are in the map. First posted on arXiv 11 Dec 2023.

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43 of 78 references.

References · 43

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31 more
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