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Tree-Weighting for Multi-Study Ensemble Learners

Multi-study learning uses multiple training studies, separately trains classifiers on each, and forms an ensemble with weights rewarding members with better cross-study prediction ability. This article considers novel weighting approaches for constructing tree-based ensemble learners in this setting...

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Bibliografische gegevens
Gepubliceerd in:Pac Symp Biocomput
Hoofdauteurs: Ramchandran, Maya, Patil, Prasad, Parmigiani, Giovanni
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: 2020
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Online toegang:https://ncbi.nlm.nih.gov/pmc/articles/PMC6980320/
https://ncbi.nlm.nih.gov/pubmed/31797618
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