Variable selection using random forests

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Random forests for genomic data analysis

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A tutorial review: Metabolomics and partial least squares-discriminant analysis – a marriage of convenience or a shotgun wedding

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Understanding variable importances in forests of randomized trees

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A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources

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Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest

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Evaluation of variable selection methods for random forests and omics data sets

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Analysis of a Random Forests ModelPreprint

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Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest

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An exploratory study of the pull-based software development model

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VSURF: An R Package for Variable Selection Using Random Forests

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A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost

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Novel phosphate-solubilizing bacteria enhance soil phosphorus cycling following ecological restoration of land degraded by mining

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Prediction of the landslide susceptibility: Which algorithm, which precision?

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Machine learning and deep learning—A review for ecologists

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Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach

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Machine learning assisted materials design and discovery for rechargeable batteries

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Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers

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Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models

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Variable selection using Mean Decrease Accuracy and Mean Decrease Gini based on Random Forest

Han, H., Guo, X., & Hua, Y. (2016) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess.2016.7883053 · Cited by 448

Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects

Alam, G., Ihsanullah, I., Naushad, M., & Sillanpää, M. E. T. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2021.130011 · Cited by 402

Analysis of machine learning techniques for heart failure readmissions

Mortazavi, B. J., Downing, N. S., Bucholz, E. M., Dharmarajan, K., Manhapra, A., Li, S.-X., Negahban, S. N., & Krumholz, H. M. (2016) · Circulation: Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.116.003039 · Cited by 401

Classification and prediction of diabetes disease using machine learning paradigm

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Random Forests for Big Data

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Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran

Zeraatpisheh, M., Ayoubi, S., Jafari, A., Tajik, S., & Finke, P. (2018) · Geoderma · doi:10.1016/j.geoderma.2018.09.006 · Cited by 376

Random forests for global sensitivity analysis: A selective review

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Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey

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Application of machine learning to construction injury prediction

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Rumor Detection over Varying Time Windows

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Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China

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Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

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Predicting factors for survival of breast cancer patients using machine learning techniques

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Text-based crude oil price forecasting: A deep learning approach

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Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships

Janet, J. P., & Kulik, H. J. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.7b08750 · Cited by 289

Mining data with random forests: current options for real‐world applications

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Improved Early Crop Type Identification By Joint Use of High Temporal Resolution SAR And Optical Image Time Series

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Comparison of variable selection methods for clinical predictive modeling

Sanchez-Pinto, L. N., Venable, L. R., Fahrenbach, J. P., & Churpek, M. M. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.05.006 · Cited by 294

A comparison among interpretative proposals for Random Forests

Aria, M., Cuccurullo, C., & Gnasso, A. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100094 · Cited by 280

Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival

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A new variable selection approach using Random Forests

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Altered type II interferon precedes autoantibody accrual and elevated type I interferon activity prior to systemic lupus erythematosus classification

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Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

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Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data

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Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology

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Biochar application as a tool to decrease soil nitrogen losses ( NH 3 volatilization, N2O emissions, and N leaching) from croplands: Options and mitigation strength in a global perspective

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Innovation in rangeland monitoring: annual, 30 m, plant functional type percent cover maps for U.S. rangelands, 1984–2017

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Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

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Data quantity governance for machine learning in materials science

Liu, Y., Yang, Z., Zou, X., Ma, S., Liu, D., Avdeev, M., & Shi, S. (2023) · National Science Review · doi:10.1093/nsr/nwad125 · Cited by 243

Early identification of emerging technologies: A machine learning approach using multiple patent indicators

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A methodology to derive global maps of leaf traits using remote sensing and climate data

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An empirical comparison of machine learning techniques for dam behaviour modelling

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Mapping maximum urban air temperature on hot summer days

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Detection of Asphalt Pavement Potholes and Cracks Based on the Unmanned Aerial Vehicle Multispectral Imagery

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A global analysis of the social and environmental outcomes of community forests

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A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat

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Spectral band selection for vegetation properties retrieval using Gaussian processes regression

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Similarity-based Android malware detection using Hamming distance of static binary features

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Statistical model optimized random forest regression model for concrete dam deformation monitoring

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Bacterial and viral respiratory tract microbiota and host characteristics in children with lower respiratory tract infections: a matched case-control study

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A comparison of urban heat islands mapped using skin temperature, air temperature, and apparent temperature (Humidex), for the greater Vancouver area

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Variable Selection in Time Series Forecasting Using Random Forests

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A review on lubricant condition monitoring information analysis for maintenance decision support

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iForest: Interpreting Random Forests via Visual Analytics

Zhao, X., Wu, Y., Lee, D. L., & Cui, W. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864475 · Cited by 204

A new variable importance measure for random forests with missing data

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Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species

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Ensemble machine learning models for the detection of energy theft

Gunturi, S. K., & Sarkar, D. (2020) · Electric Power Systems Research · doi:10.1016/j.epsr.2020.106904 · Cited by 205

Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models

Bikmukhametov, T., & Jäschke, J. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.106834 · Cited by 202

The potency of defects on fatigue of additively manufactured metals

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Two-layer fuzzy multiple random forest for speech emotion recognition in human-robot interaction

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Data-Driven Approach for Fault Detection and Diagnostic in Semiconductor Manufacturing

Fan, S. S., Hsu, C.-Y., Tsai, D., He, F., & Cheng, C.-C. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.2983061 · Cited by 195

A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta, China

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Multi-method ensemble selection of spectral bands related to leaf biochemistry

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A comparison of random forest and support vector machine approaches to predict coal spontaneous combustion in gob

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Comparison of random forests and other statistical methods for the prediction of lake water level: a case study of the Poyang Lake in China

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Land subsidence modelling using tree-based machine learning algorithms

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Real-time classification for autonomous drowsiness detection using eye aspect ratio

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Stream water quality prediction using boosted regression tree and random forest models

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Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests

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Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms

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Conditional permutation importance revisited

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Prediction of Bus Travel Time Using Random Forests Based on Near Neighbors

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Severe fire weather and intensive forest management increase fire severity in a multi‐ownership landscape

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Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

Milanovic, S., Marković, N., Pamucar, D., Gigović, L., Kostić, P., & Milanović, S. D. (2020) · Forests · doi:10.3390/f12010005 · Cited by 150

A framework for feature selection through boosting

Alsahaf, A. M. J., Petkov, N., Shenoy, V., & Azzopardi, G. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115895 · Cited by 178

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

Knox, S., Bansal, S., McNicol, G., Schäfer, K. V. R., Sturtevant, C., Ueyama, M., Valach, A. C., Baldocchi, D., Delwiche, K., Desai, A. R., et al. (2021) · Global Change Biology · doi:10.1111/gcb.15661 · Cited by 177

Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data

Tramontana, G., Ichii, K., Camps‐Valls, G., Tomelleri, E., & Papale, D. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.07.015 · Cited by 174

Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery

Liu, Y., Gong, W., Xing, Y., Hu, X., & Gong, J. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.03.016 · Cited by 174

Cost-sensitive feature selection using random forest: Selecting low-cost subsets of informative features

Zhou, Q., Zhou, H., & Li, T. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.11.010 · Cited by 176

Soil Salinity Mapping Using SAR Sentinel-1 Data and Advanced Machine Learning Algorithms: A Case Study at Ben Tre Province of the Mekong River Delta (Vietnam)

Hoa, P. V., Nguyen, V. G., Bình, N. A., Hai, L. V. H., Pham, T. D., Hasanlou, M., & Bui, D. T. (2019) · Remote Sensing · doi:10.3390/rs11020128 · Cited by 172

Random Forests: some methodological insightsPreprint

Genuer, R., Poggi, J., & Tuleau-Malot, C. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0811.3619 · Cited by 204

Random Forests Variable Selection

Ishwaran, H. (2025) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003493679-17

Random forests variable importances Towards a better understanding and large-scale feature selection

Sutera, A. (2016) · ORBi (University of Liège)

Identification of Statistically Significant Features from Random Forests

Paul, J., Verleysen, M., & Dupont, P. (2013) · Cited by 10

VSURF: An R Package for Variable Selection Using Random Forests

Genuer, R., Poggi, J., & Tuleau-Malot, C. (2015) · The R Journal · doi:10.32614/rj-2015-018 · Cited by 633