Learning from high dimensional data based on weighted feature importance in decision tree ensembles
From MaRDI portal
Publication:6538421
DOI10.1007/s00180-023-01347-3MaRDI QIDQ6538421
Nayiri Galestian Pour, Soudabeh Shemehsavar
Publication date: 14 May 2024
Published in: Computational Statistics (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- Bagging predictors
- BART: Bayesian additive regression trees
- Random survival forests
- Classification by ensembles from random partitions of high-dimensional data
- A decision-theoretic generalization of on-line learning and an application to boosting
- A new ensemble method with feature space partitioning for high-dimensional data classification
- Bayesian Regression Trees for High-Dimensional Prediction and Variable Selection
- Random-projection Ensemble Classification
- Reinforcement Learning Trees
- Extremely randomized trees
- Random forests
- Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival
This page was built for publication: Learning from high dimensional data based on weighted feature importance in decision tree ensembles