Improving random forest algorithm by Lasso method

DOI10.1080/00949655.2020.1814776OpenAlexW3083707628MaRDI QIDQ5033957

Hui Wang, Guizhi Wang

Publication date: 24 February 2022

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2020.1814776

zbMATH Keywords

predictiondecision treesLassorandom forestensemble learningpost-selection

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• GeneSrF
• AdaBoost.MH

Cites Work

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• Greedy function approximation: A gradient boosting machine.
• Bagging predictors
• Bootstrap methods: another look at the jackknife
• A decision-theoretic generalization of on-line learning and an application to boosting
• A general lower bound on the number of examples needed for learning
• A new variable selection approach using random forests
• Machine Learning: ECML 2004
• Signal Recovery by Proximal Forward-Backward Splitting
• Random forests
• Linear programming boosting via column generation

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