Counterfactual Explanation of Machine Learning Survival Models

DOI10.15388/21-INFOR468zbMath1486.68153arXiv2006.16793MaRDI QIDQ5862149

Maxim S. Kovalev, Frank P. A. Coolen, Lev V. Utkin, Unnamed Author

Publication date: 4 March 2022

Published in: Informatica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.16793

zbMATH Keywords

convex optimizationsurvival analysisCox modelcensored dataparticle swarm optimizationexplainable AIinterpretable modelcounterfactual explanation

Mathematics Subject Classification ID

Convex programming (90C25) Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Reliability and life testing (62N05)

Related Items (1)

Note on the bi-risk discrete time risk model with income rate two

Uses Software

• shap
• SVRc
• DeepSurv
• CERTIFAI

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• A mixed-discrete particle swarm optimization algorithm with explicit diversity-preservation
• A comprehensive survey on particle swarm optimization algorithm and its applications
• A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds
• Recursive partitioning for heterogeneous causal effects
• Analysis of Survival Data with Group Lasso
• Assessing Influence in Regression Analysis with Censored Data
• Adaptive Lasso for Cox's proportional hazards model
• Random forests

This page was built for publication: Counterfactual Explanation of Machine Learning Survival Models