Multiobjective semisupervised learning with a right‐censored endpoint adapted to the multiple imputation framework

DOI10.1002/bimj.202000365zbMath1523.62114OpenAlexW3175337053MaRDI QIDQ6068873

Unnamed Author, Unnamed Author, Sylvie Chevret

Publication date: 15 December 2023

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.202000365

zbMATH Keywords

multiobjective optimizationmultiple imputationconsensussemisupervised learningsurvival endpoint

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Unnamed Item
• Unnamed Item
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• Mixture model clustering for mixed data with missing information
• Random survival forests
• A fast and recursive algorithm for clustering large datasets with \(k\)-medians
• Direct multicriteria clustering algorithms
• Identification of relevant subtypes via preweighted sparse clustering
• Clustering method for censored and collinear survival data
• Concordance probability and discriminatory power in proportional hazards regression
• Introduction to Semi-Supervised Learning
• Inference and missing data
• 10.1162/153244303321897735
• Evaluating Prediction Rules fort-Year Survivors With Censored Regression Models
• Clustering multiply imputed multivariate high‐dimensional longitudinal profiles
• k-POD: A Method for k-Means Clustering of Missing Data
• The elements of statistical learning. Data mining, inference, and prediction

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