ArticleExpert systems with applications2019
A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling.
Article in Expert systems with applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 254 papers, 2 of them syntheses that pooled it.
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Who cites it
254 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Development and Validation of Machine-Learning-Based Prediction Models for Thyroid Diseases During Pregnancy.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
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- IALA-BPNN: superior house price prediction through multi-strategy optimized Artificial Lemming Algorithm and BP neural network.Scientific reports · 2026Article
- CataCon: a contrastive graph representation learning framework for catalyst prediction.Journal of cheminformatics · 2026Article
- ShadowVIMP: permutation-based multiple testing-controlled variable selection.BMC bioinformatics · 2026Article
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- Exploring an Intermediate Colorectal Cancer Screening Test Based on Stool Proteomics and Machine Learning for Optimizing the Selection of Patients for Colonoscopy Identified From FIT.Molecular & cellular proteomics : MCP · 2026Article
- Early risk stratification of acute kidney injury severity in patients with acute myocardial infarction and diabetes using machine learning.Scientific reports · 2026Article
194 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
Random forest classification is a popular machine learning method for developing prediction models in many research settings. Often in prediction modeling, a goal is to reduce the number of variables needed to obtain a prediction in order to reduce the burden of data collection and improve efficiency. Several variable selection methods exist for the setting of random forest classification; however, there is a paucity of literature to guide users as to which method may be preferable for different types of datasets. Using 311 classification datasets freely available online, we evaluate the prediction error rates, number of variables, computation times and area under the receiver operating curve for many random forest variable selection methods. We compare random forest variable selection methods for different types of datasets (datasets with binary outcomes, datasets with many predictors, and datasets with imbalanced outcomes) and for different types of methods (standard random forest versus conditional random forest methods and test based versus performance based methods). Based on our study, the best variable selection methods for most datasets are Jiang's method and the method implemented in the
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.