SurvBeNIM: The Beran-Based Neural Importance Model for Explaining Survival Models
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 incor...
Saved in:
Main Authors: | Lev V. Utkin, Danila Y. Eremenko, Andrei V. Konstantinov, Vladimir A. Muliukha |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10858699/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Dual feature-based and example-based explanation methods
by: Andrei Konstantinov, et al.
Published: (2025-02-01) -
Balancing Explainability and Privacy in Bank Failure Prediction: A Differentially Private Glass-Box Approach
by: Junyoung Byun, et al.
Published: (2025-01-01) -
A Study on the Application of Explainable AI on Ensemble Models for Predictive Analysis of Chronic Kidney Disease
by: K. M. Tawsik Jawad, et al.
Published: (2025-01-01) -
Customization of health insurance premiums using machine learning and explainable AI
by: Manohar Kapse, et al.
Published: (2025-06-01) -
Reliability analysis of new jointly Type-II hybrid NH censored data and its modeling for three engineering cases
by: Maysaa Elmahi Abd Elwahab, et al.
Published: (2025-02-01)