A weighted random forests approach to improve predictive performance

DOI10.1002/sam.11196zbMath1281.62238OpenAlexW1885645786WikidataQ37549318 ScholiaQ37549318MaRDI QIDQ2870757

Joanna M. Biernacka, Stacey J. Winham, Robert R. Freimuth

Publication date: 21 January 2014

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3912194

zbMATH Keywords

high-dimensional dataweightinggenome-wide associationgenetic datagene interactions

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Biochemistry, molecular biology (92C40) Genetics and epigenetics (92D10)

Related Items (4)

A random forest guided tour ⋮ Ordinal trees and random forests: score-free recursive partitioning and improved ensembles ⋮ Tree aggregation for random forest class probability estimation ⋮ On Cesáro averages for weighted trees in the random forest

Cites Work

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• A weight-adjusted voting algorithm for ensembles of classifiers
• Machine Learning: ECML 2004
• Random forests
• Estimating generalization error on two-class datasets using out-of-bag estimates

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