Variable importance-weighted Random Forests
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Summary
Variable importance-weighted Random Forests is proposed, which instead of sampling features with equal probability at each node to build up trees, samples features according to their variable importance scores, and then select the best split from the randomly selected features.
- Type
- article
- Published
- 2017-11-06
- Cited by
- 105
- References
- 18
- Access
- Open access
- OpenAlex
- https://openalex.org/W2767419625
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:25423607
Keywords
Random forest, Feature selection, Feature (linguistics), Variable (mathematics), Computer science
References
- Gene selection and classification of microarray data using random forest
- Random Forest Models To Predict Aqueous Solubility
- Enriched random forests
- The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity
- Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
- Prediction of central nervous system embryonal tumour outcome based on gene expression
- A Conditional t Suite of Tests for Identifying Differentially Expressed Genes in a DNA Microarray Experiment with Little Replication
- Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products
- MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features
- Hallmarks of cancer: the next generation.
- An extensive comparison of recent classification tools applied to microarray data
- Result Analysis of the NIPS 2003 Feature Selection Challenge
- Gene expression correlates of clinical prostate cancer behavior.
- Random Forests
- Analysis of a Random Forests Model
- Random Forests for Genetic Association Studies
- Statistical modeling: The two cultures
- Statistical Applications in Genetics and Molecular Biology Random Forests for Genetic Association Studies
- Application of Breiman's Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules
Cited by
- Predicting Deviation in Supplier Lead Time and Truck Arrival Time Using Machine Learning - A Data Mining Project at Volvo Group
- A combined drought monitoring index based on multi-sensor remote sensing data and machine learning
- An instance and variable selection approach in pixel-based classification for automatic white blood cells segmentation
- Machine-learning algorithms for predicting results in liver transplantation: the problem of donor-recipient matching.
- Genetic expression and mutational profile analysis in different pathologic stages of hepatocellular carcinoma patients
- Machine learning-based long-term outcome prediction in patients undergoing percutaneous coronary intervention.
- Applying random forest in a health administrative data context: a conceptual guide
- Discriminating Xylella fastidiosa from Verticillium dahliae infections in olive trees using thermal- and hyperspectral-based plant traits
- Landscape of Immune Microenvironment in Epithelial Ovarian Cancer and Establishing Risk Model by Machine Learning
- Divergent abiotic spectral pathways unravel pathogen stress signals across species
- Multi‐view rank‐based random forest: A new algorithm for prediction in eSports
- Establishment of a Preoperative Laboratory Panel to identify Lymph Node Metastasis in Superficial Esophageal Cancer
- A Framework on Analyzing Long-Term Drought Changes and Its Influential Factors Based on the PDSI
- The use of machine learning for investigating the role of plastic surgeons in anatomical injuries: A retrospective observational study
- A transfer learning approach based on random forest with application to breast cancer prediction in underrepresented populations.
- When Machine Learning Meets Social Science: A Comparative Study of Ordinary Least Square, Stochastic Gradient Descent, and Support Vector Regression for Exploring the Determinants of Behavioral Intentions to Tuberculosis Screening
- Effect of Parthenium hysterophorus L. Invasion on Soil Microbial Communities in the Yellow River Delta, China
- Prediction of Polygenic Risk Score by Machine Learning and Deep Learning Methods in Genome-wide Association Studies
- Loose Particle Localization Method for Sealed Electronic Equipment Based on Improved Random Forest
- Learning from high dimensional data based on weighted feature importance in decision tree ensembles
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