Variable selection using random forests

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Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

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Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling

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Using GIS and Random Forests to identify fire drivers in a forest city, Yichun, China

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A Random Forests-based sensitivity analysis framework for assisted history matching

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Rethinking fire‐adapted species in an altered fire regime

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Feature Selection Based on Random Forest for Partial Discharges Characteristic Set

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Multi-Perspective Crude Oil Price Forecasting with a New Decomposition-Ensemble Framework

Guo, J., Zhao, Z., Sun, J., & Sun, S. (2021) · Resources Policy · doi:10.1016/j.resourpol.2022.102737 · Cited by 50

Mapping long-term land use and land cover change in the central Himalayan region using a tree-based ensemble classification approach

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Multivariable Fingerprints With Random Forest Variable Selection for Indoor Positioning System

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Assessing the upper elevational limits of vegetation growth in global high-mountains

Zou, L., Tian, F., Liang, T., Eklundh, L., Tong, X., Tagesson, T., Dou, Y., He, T., Liang, S., & Fensholt, R. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2022.113423 · Cited by 46

A unified ML framework for solubility prediction across organic solvents

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Predicting the delay of issues with due dates in software projects

Choetkiertikul, M., Dam, H. K., Tran, T., & Ghose, A. (2017) · Empirical Software Engineering · doi:10.1007/s10664-016-9496-7 · Cited by 46

Mapping Prosopis glandulosa (mesquite) in the semi-arid environment of South Africa using high-resolution WorldView-2 imagery and machine learning classifiers

Adam, E. M. I., Mureriwa, N., & Newete, S. W. (2017) · Journal of Arid Environments · doi:10.1016/j.jaridenv.2017.05.001 · Cited by 46

Projecting Soil Organic Carbon Distribution in Central Chile under Future Climate Scenarios

Rojas, L. A. R., Adhikari, K., & Ventura, S. J. (2018) · Journal of Environmental Quality · doi:10.2134/jeq2017.08.0329 · Cited by 46

Combining clustering of variables and feature selection using random forests

Marie Chavent, A., Genuer, R., & Saracco, J. (2019) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2018.1563145 · Cited by 46

Combination of Hyperspectral and Machine Learning to Invert Soil Electrical Conductivity

Jia, P., Zhang, J., He, W., Hu, Y. C., Zeng, R., Zamanian, K., Jia, K., & Zhao, X. (2022) · Remote Sensing · doi:10.3390/rs14112602 · Cited by 45

Vegetation recovery drivers at short-term after fire are plant community-dependent in mediterranean burned landscapes

Fernández‐Guisuraga, J. M., Fernandes, P. M., Tárrega, R., Beltrán-Marcos, D., & Calvo, L. (2023) · Forest Ecology and Management · doi:10.1016/j.foreco.2023.121034 · Cited by 49

Identifying key landscape pattern indices influencing the NPP: A case study of the upper and middle reaches of the Yellow River

Xue, S., Ma, B., Wang, C., & Li, Z. (2023) · Ecological Modelling · doi:10.1016/j.ecolmodel.2023.110457 · Cited by 45

Woods: A fast and accurate functional annotator and classifier of genomic and metagenomic sequences

Sharma, A. K., Gupta, A., Kumar, S., Dhakan, D. B., & Sharma, V. K. (2015) · Genomics · doi:10.1016/j.ygeno.2015.04.001 · Cited by 45

An inductive typology of egocentric networks

Giannella, E., & Fischer, C. S. (2016) · Social Networks · doi:10.1016/j.socnet.2016.02.003 · Cited by 45

The role of biofilm forming on mortality in patients with candidemia: a study derived from real world data

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Estimation of Climatologies of Average Monthly Air Temperature over Mongolia Using MODIS Land Surface Temperature (LST) Time Series and Machine Learning Techniques

Otgonbayar, M., Atzberger, C., Mattiuzzi, M., & Erdenedalai, A. (2019) · Remote Sensing · doi:10.3390/rs11212588 · Cited by 45

Prediction and causal analysis of defects in steel products: Handling nonnegative and highly overdispersed count data

Zhang, X., Kano, M., Tani, M., Mori, J., Ise, J., & Harada, K. (2019) · Control Engineering Practice · doi:10.1016/j.conengprac.2019.104258 · Cited by 45

Laser induced breakdown spectroscopy for elemental analysis and discrimination of honey samples

Stefas, D., Gyftokostas, N., & Couris, S. (2020) · Spectrochimica Acta Part B Atomic Spectroscopy · doi:10.1016/j.sab.2020.105969 · Cited by 45

Land degradation risk dynamics assessment in red and lateritic zones of eastern plateau, India: A combine approach of K-fold CV, data mining and field validation

Saha, A., Pal, S. C., Chowdhuri, I., Islam, A. R. M. T., Roy, P., & Chakrabortty, R. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101653 · Cited by 45

A Bayesian approach to consequent parameter estimation in probabilistic fuzzy systems and its application to bearing fault classification

Li, C., Melo, L. L. M., Delgado, M., Cerrada, M., Pacheco, F., Cabrera, D. R., Sánchez, R., & de Oliveira, J. V. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.05.007 · Cited by 44

Selecting optimal conditioning parameters for landslide susceptibility: an experimental research on Aqabat Al-Sulbat, Saudi Arabia

Alqadhi, S. D., Mallick, J., Talukdar, S., Bindajam, A. A., Hong, N. V., & Saha, T. K. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-15886-z · Cited by 44

A deep learning process anomaly detection approach with representative latent features for low discriminative and insufficient abnormal data

Gao, Y., Yin, X., He, Z., & Wang, X. (2022) · Computers & Industrial Engineering · doi:10.1016/j.cie.2022.108936 · Cited by 44

The patterns and driving forces of dengue invasions in China

Zhao, Z., Yue, Y., Liu, X., Li, C., Ma, W., & Liu, Q. (2023) · Infectious Diseases of Poverty · doi:10.1186/s40249-023-01093-0 · Cited by 44

Algorithms going wild – A review of machine learning techniques for terrestrial ecology

Cipriano, C., Noce, S., Mereu, S., & Santini, M. (2025) · Ecological Modelling · doi:10.1016/j.ecolmodel.2025.111164 · Cited by 50

Recursive Random Forests Enable Better Predictive Performance and Model Interpretation than Variable Selection by LASSO

Zhu, X., Xin, Y., & Ge, H.-L. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500715e · Cited by 44

Building Collapse Assessment in Urban Areas Using Texture Information From Postevent SAR Data

Sun, W., Shi, L., Yang, J., & Li, P. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2580610 · Cited by 44

Consistent estimation of residual variance with random forest Out-Of-Bag errors

Ramosaj, B., & Pauly, M. (2019) · Statistics & Probability Letters · doi:10.1016/j.spl.2019.03.017 · Cited by 44

Development of a hybrid scoring system for EOR screening by combining conventional screening guidelines and random forest algorithm

Zhang, N., Wei, M., Fan, J., Aldhaheri, M. N., Zhang, Y., & Bai, B. (2019) · Fuel · doi:10.1016/j.fuel.2019.115915 · Cited by 44

Machine learning-based motor assessment of Parkinson’s disease using postural sway, gait and lifestyle features on crowdsourced smartphone data

Abujrida, H., Agu, E., & Pahlavan, K. (2019) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ab39a8 · Cited by 45

Individualized treatment response prediction of dialectical behavior therapy for borderline personality disorder using multimodal magnetic resonance imaging

Schmitgen, M. M., Niedtfeld, I., Schmitt, R., Mancke, F., Winter, D., Schmahl, C. G., & Herpertz, S. C. (2019) · Brain and Behavior · doi:10.1002/brb3.1384 · Cited by 44

Climate and soils at the Brazilian semiarid and the forest-Caatinga problem: new insights and implications for conservation

Oliveira, G., Francelino, M. R., Arruda, D. M., Filho, E. I. F., & Schaefer, C. E. G. R. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab3d7b · Cited by 44

Classifying grass-dominated habitats from remotely sensed data: The influence of spectral resolution, acquisition time and the vegetation classification system on accuracy and thematic resolution

Bradter, U., O’Connell, J., Kunin, W. E., Boffey, C. W. H., Ellis, R. J., & Benton, T. G. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.134584 · Cited by 44

Assessing, mapping, and optimizing the locations of sediment control check dams construction

Pourghasemi, H. R., Yousefi, S., Sãdhasivam, N., & Eskandari, S. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.139954 · Cited by 44

Groundwater-dependent ecosystems as transfer vectors of nitrogen from the aquifer to surface waters in agricultural basins: The fontanili of the Po Plain (Italy)

Balestrini, R., Delconte, C. A., Sacchi, E., & Buffagni, A. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.141995 · Cited by 44

Discrimination of cracked soybean seeds by near-infrared spectroscopy and random forest variable selection

Wang, L., Huang, Z., & Wang, R. (2021) · Infrared Physics & Technology · doi:10.1016/j.infrared.2021.103731 · Cited by 45

Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models

Schiessler, E. J., Würger, T., Lamaka, S. V., Meißner, R. H., Cyron, C. J., Zheludkevich, M. L., Feiler, C., & Aydin, R. C. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00658-7 · Cited by 44

Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data

Alonso-Rego, C., Arellano-Pérez, S., Guerra-Hernández, J., Molina-Valero, J. A., Martínez-Calvo, A., Pérez‐Cruzado, C., Castedo‐Dorado, F., González‐Ferreiro, E., Álvarez‐González, J. G., & Ruiz-González, A. D. (2021) · Remote Sensing · doi:10.3390/rs13245170 · Cited by 49

Current Progress of High-Throughput MicroRNA Differential Expression Analysis and Random Forest Gene Selection for Model and Non-Model Systems: an R Implementation

Zhang, J., Hadj‐Moussa, H., & Storey, K. B. (2016) · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics · doi:10.1515/jib-2016-306 · Cited by 44

Machine learning algorithm for characterizing risks of hypertension, at an early stage in Bangladesh

Islam, M. M., Rahman, M. J., Roy, D. C., Tawabunnahar, M., Jahan, R., Ahmed, N. A. M. F., & Maniruzzaman, M. (2021) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2021.03.035 · Cited by 43

Evaluation of machine learning algorithms to classify and map landforms in Antarctica

Siqueira, R. G., Veloso, G. V., Filho, E. I. F., Francelino, M. R., Schaefer, C. E. G. R., & Corrêa, G. R. (2021) · Earth Surface Processes and Landforms · doi:10.1002/esp.5253 · Cited by 45

Determining the association of the built environment and socioeconomic attributes with urban shrinking in Yokohama City

Ma, S., Kumakoshi, Y., Koizumi, H., & Yoshimura, Y. (2021) · Cities · doi:10.1016/j.cities.2021.103474 · Cited by 43

Automatic language analysis identifies and predicts schizophrenia in first-episode of psychosis

Figueroa-Barra, A. I., Aguila, D. D., Cerda, M., Gaspar, P. A., Terissi, L. D., Durán, M., & Valderrama, C. (2022) · Schizophrenia · doi:10.1038/s41537-022-00259-3 · Cited by 43

Dam Water Level Prediction Using Vector AutoRegression, Random Forest Regression and MLP-ANN Models Based on Land-Use and Climate Factors

Ouma, Y. O., Moalafhi, D. B., Anderson, G., Nkwae, B., Odirile, P., Parida, B. P., & Qi, J. (2022) · Sustainability · doi:10.3390/su142214934 · Cited by 43

Canopy Gap Mapping from Airborne Laser Scanning: An Assessment of the Positional and Geometrical Accuracy

Bonnet, S., Gaulton, R. G., Lehaire, F., & Lejeune, P. (2015) · Remote Sensing · doi:10.3390/rs70911267 · Cited by 43

A comprehensive study of eleven feature selection algorithms and their impact on text classification

Vora, S., & Yang, H. Q. (2017) · 2017 Computing Conference · doi:10.1109/sai.2017.8252136 · Cited by 43

Deep understanding in industrial processes by complementing human expertise with interpretable patterns of machine learning

Ragab, A., El-Koujok, M., Ghezzaz, H., Amazouz, M., Ouali, M.-S., & Yacout, S. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.01.011 · Cited by 47

A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

Mallinis, G., Chrysafis, I., Korakis, G., Pana, E., & Kyriazopoulos, A. P. (2020) · Remote Sensing · doi:10.3390/rs12071210 · Cited by 44

A novel machine-learning algorithm for predicting mortality risk after hip fracture surgery

Li, Y., Chen, M., Lv, H., Yin, P., Zhang, L., & Tang, P. (2020) · Injury · doi:10.1016/j.injury.2020.12.008 · Cited by 43

Internet penetration as national innovation capacity: worldwide evidence on the impact of ICTs on innovation development

Xiong, F., Zang, L., & Gao, Y. (2021) · Information Technology for Development · doi:10.1080/02681102.2021.1891853 · Cited by 43

Predicting the NOx emissions of low heat value gas rich-quench-lean combustor via three integrated learning algorithms with Bayesian optimization

Yan, P., Fan, W., & Zhang, R. (2023) · Energy · doi:10.1016/j.energy.2023.127227 · Cited by 42

Lateralization of Temporal Lobe Epilepsy Based on Resting-State Functional Magnetic Resonance Imaging and Machine Learning

Yang, Z., Choupan, J., Reutens, D. C., & Hocking, J. (2015) · Frontiers in Neurology · doi:10.3389/fneur.2015.00184 · Cited by 42

Trade-offs between plant species richness and carbon storage in the context of afforestation – Examples from afforestation scenarios in the Mulde Basin, Germany

Lautenbach, S., Jungandreas, A., Blanke, J. H., Lehsten, V., Mühlner, S., Kühn, I., & Völk, M. (2016) · Ecological Indicators · doi:10.1016/j.ecolind.2016.09.035 · Cited by 42

Short-Term Traffic Flow Forecasting for Urban Roads Using Data-Driven Feature Selection Strategy and Bias-Corrected Random Forests

Ou, J., Xia, J., Wu, Y., & Rao, W. (2017) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2645-17 · Cited by 42

Classification of Tree Species in a Diverse African Agroforestry Landscape Using Imaging Spectroscopy and Laser Scanning

Piiroinen, R., Heiskanen, J., Maeda, E. E., Viinikka, A., & Pellikka, P. (2017) · Remote Sensing · doi:10.3390/rs9090875 · Cited by 42

High-resolution remote sensing and advanced classification techniques for the prospection of archaeological sites’ markers: The case of dung deposits in the Shashi-Limpopo Confluence area (southern Africa)

Thabeng, O. L., Merlo, S., & Adam, E. (2018) · Journal of Archaeological Science · doi:10.1016/j.jas.2018.12.003 · Cited by 42

Historical Redlining, Social Determinants of Health, and Stroke Prevalence in Communities in New York City

Jadow, B. M., Hu, L., Zou, J., Labovitz, D., Ibeh, C., Ovbiagele, B., & Esenwa, C. C. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.5875 · Cited by 42

Uncertainty quantification study of the physics-informed machine learning models for critical heat flux prediction

Mao, C., & Jin, Y. (2024) · Progress in Nuclear Energy · doi:10.1016/j.pnucene.2024.105097 · Cited by 42

Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data

Aksu, Ş. H., Çakıt, E., & Dağdeviren, M. (2024) · Applied Sciences · doi:10.3390/app14062282 · Cited by 41

Machine learning predicts heavy metal adsorption on iron (oxyhydr)oxides: A combined insight into the adsorption efficiency and binding configuration

Liu, J., Zhao, J., Du, J., Peng, S., Tan, S., Zhang, W., Xu, Y., Wang, H., & Lin, Z. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.175370 · Cited by 41

Multiple indicator gases and temperature prediction of coal spontaneous combustion oxidation process

Lei, C., Feng, Q., Zhu, Y., Cui, C., Bao, R., & Deng, C. (2025) · Fuel · doi:10.1016/j.fuel.2025.134991 · Cited by 41

A Random Forest approach to predict the spatial distribution of sediment pollution in an estuarine system

Walsh, E. S., Kreakie, B. J., Cantwell, M. G., & Nacci, D. E. (2017) · PLoS ONE · doi:10.1371/journal.pone.0179473 · Cited by 41

Testing random forest classification for identifying lava flows and mapping age groups on a single Landsat 8 image

Li, L., Solana, C., Canters, F., & Kervyn, M. (2017) · Journal of Volcanology and Geothermal Research · doi:10.1016/j.jvolgeores.2017.07.014 · Cited by 41

Numerical Assessments of Leaf Area Index in Tropical Savanna Rangelands, South Africa Using Landsat 8 OLI Derived Metrics and In-Situ Measurements

Dube, T. J., Pandit, S., Shoko, C., Ramoelo, A., Mazvimavi, D., & Dalu, T. (2019) · Remote Sensing · doi:10.3390/rs11070829 · Cited by 41

Automatic Identification of Honeypot Server Using Machine Learning Techniques

Huang, C., Han, J., Zhang, X., & Liu, J. (2019) · Security and Communication Networks · doi:10.1155/2019/2627608 · Cited by 41

Predicting in-hospital rupture of type A aortic dissection using random forest

Wu, J., Qiu, J., Xie, E., Jiang, W., Zhao, R., Qiu, J., Zafar, M. A., Huang, Y., & Yu, C. (2019) · Journal of Thoracic Disease · doi:10.21037/jtd.2019.10.82 · Cited by 41

Regional-Scale Forest Mapping over Fragmented Landscapes Using Global Forest Products and Landsat Time Series Classification

Myroniuk, V., Kutia, M., Sarkissian, A. J., Bilous, A., & Liu, S. (2020) · Remote Sensing · doi:10.3390/rs12010187 · Cited by 41

A Deep-Learning View of Chemical Space Designed to Facilitate Drug Discovery

Maragakis, P., Nisonoff, H. M., Cole, B., & Shaw, D. E. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00321 · Cited by 43

Detecting congestive heart failure by extracting multimodal features with synthetic minority oversampling technique (SMOTE) for imbalanced data using robust machine learning techniques

Hussain, L., Lone, K. J., Awan, I. A., Abbasi, A. A., & Pirzada, J.-U.-R. (2020) · Waves in Random and Complex Media · doi:10.1080/17455030.2020.1810364 · Cited by 41

Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Gyftokostas, N., Stefas, D., Kokkinos, V., Bouras, C., & Couris, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-84941-z · Cited by 41

A Novel Classifier Ensemble Method Based on Subspace Enhancement for High-Dimensional Data Classification

Xu, Y., Yu, Z., Cao, W., & Chen, C. L. P. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3087517 · Cited by 41

Separating the wheat from the chaff

Stijven, S., Minnebo, W., & Vladislavleva, K. (2011) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/2001858.2002059 · Cited by 41

Chemical Descriptors for a Large-Scale Study on Drop-Weight Impact Sensitivity of High Explosives

Marrs, F. W., Davis, J. V., Burch, A. C., Brown, G. W., Lease, N., Huestis, P. L., Cawkwell, M. J., & Manner, V. W. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01154 · Cited by 40

Object-based classification of urban plant species from very high-resolution satellite imagery

Sicard, P., Coulibaly, F., Lameiro, M., Araminienė, V., Marco, A. D., Sorrentino, B., Anav, A., Manzini, J., Hoshika, Y., Moura, B. B., et al. (2023) · Urban forestry & urban greening · doi:10.1016/j.ufug.2023.127866 · Cited by 41

Forecasting tropical cyclone tracks in the northwestern Pacific based on a deep-learning model

Wang, L., Wan, B., Zhou, S., Sun, H., & Gao, Z. (2023) · Geoscientific model development · doi:10.5194/gmd-16-2167-2023 · Cited by 41

Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities

Chowdhury, S., & Karanfil, T. (2024) · Chemosphere · doi:10.1016/j.chemosphere.2024.141958 · Cited by 40

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Li, Z., Wang, J., Tang, H., Huang, C., Yang, F., Chen, B., Wang, X., Xin, X., & Ge, Y. (2016) · Remote Sensing · doi:10.3390/rs8080632 · Cited by 40

Methods for variable selection in LiDAR-assisted forest inventories

Moser, P., Vibrans, A. C., McRoberts, R. E., Næsset, E., Gobakken, T., Chirici, G., Mura, M., & Marchetti, M. A. (2016) · Forestry An International Journal of Forest Research · doi:10.1093/forestry/cpw041 · Cited by 40

Investigating important urban characteristics in the formation of urban heat islands: a machine learning approach

Yoo, S. (2018) · Journal Of Big Data · doi:10.1186/s40537-018-0113-z · Cited by 40

Exploring the emergence of influential users on social media during natural disasters

Yang, Y., Zhang, C., Fan, C. H., Yao, W., Huang, R., & Mostafavi, A. (2019) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2019.101204 · Cited by 40

Novel Alzheimer’s disease subtypes identified using a data and knowledge driven strategy

Mitelpunkt, A., Galili, T., Kozlovski, T., Bregman, N., Shachar, N., Marcus-Kalish, M., & Benjamini, Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-57785-2 · Cited by 40

Evaluating the bond strength of FRP-to-concrete composite joints using metaheuristic-optimized least-squares support vector regression

Zhang, J., & Wang, Y.-H. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05191-0 · Cited by 40

Representing the Metabolome with High Fidelity: Range and Response as Quality Control Factors in LC-MS-Based Global Profiling

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