Random Forests: some methodological insights
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Summary
This paper aims at confirming, known but sparse, advice for using random forests and at proposing some complementary remarks for both standard problems as well as high dimensional ones for which the number of variables hugely exceeds the sample size.
- Type
- preprint
- Published
- 2008-11-21
- Cited by
- 95
- References
- 40
- Access
- Open access
- OpenAlex
- https://openalex.org/W1678131841
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:266154596
Keywords
Random forest, Environmental resource management, Geography, Environmental science, Computer science
References
- Sélection de variables pour la classification binaire en grande dimension: comparaisons et application aux domées de biopuces
- Classification and Regression by randomForest
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- Gene selection and classification of microarray data using random forest
- Machine Learning for Sequential Data: A Review
- Multiple Classifier Systems
- Partial and Recombined Estimators for Nonlinear Additive Models
- An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
- Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks
- Relative Importance for Linear Regression in R: The Package relaimpo
- Bias in random forest variable importance measures: Illustrations, sources and a solution
- Systematic variation in gene expression patterns in human cancer cell lines
- Empirical characterization of random forest variable importance measures
- Wrappers for Feature Subset Selection
- Estimators of Relative Importance in Linear Regression Based on Variance Decomposition
- Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays.
- Prediction of central nervous system embryonal tumour outcome based on gene expression
- A molecular signature of metastasis in primary solid tumors
- L1‐regularization path algorithm for generalized linear models
- Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Cited by
- Finding Genes Related to Disease Using Statistical Learning
- Rapid Feature Selection Based on Random Forests for High-Dimensional Data
- Mining data with random forests: current options for real‐world applications
- Many Hands Make Light Work - On Ensemble Learning Techniques for Data Fusion in Remote Sensing
- Uncovering Bivariate Interactions in High Dimensional Data Using Random Forests with Data Augmentation
- One class random forests
- Estimation of seismic building structural types using multi-sensor remote sensing and machine learning techniques
- Important variable assessment and electricity price forecasting based on regression tree models: classification and regression trees, Bagging and Random Forests
- A new variable importance measure for random forests with missing data
- Modeling of steelmaking process with effective machine learning techniques
- A genome-wide association study of Alzheimer’s disease using random forests and enrichment analysis
- Utilizing ECG-based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification
- Variable Importance Assessment in Regression: Linear Regression versus Random Forest
- The use of classification trees for bioinformatics
- Random survival forests for high‐dimensional data
- Variable selection using random forests
- An application of Random Forests to a genome-wide association dataset: Methodological considerations & new findings
- r2VIM: A new variable selection method for random forests in genome-wide association studies
- Analyse Statistique de la Pollution par les PM10 en Haute-Normandie
- Data mining with Random Forests as a methodology for biomedical signal classification
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