Variable selection using random forests

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Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest

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Identification of Forested Landslides Using LiDar Data, Object-based Image Analysis, and Machine Learning Algorithms

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Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale

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What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests

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Tuning parameters in random forests

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Prediction models of soil heavy metal(loid)s concentration for agricultural land in Dongli: A comparison of regression and random forest

Wang, H., Yilihamu, Q., Yuan, M., Bai, H., Xu, H., & Wu, J. F. (2020) · Ecological Indicators · doi:10.1016/j.ecolind.2020.106801 · Cited by 163

Dysregulation of innate and adaptive serum mediators precedes systemic lupus erythematosus classification and improves prognostic accuracy of autoantibodies

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A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation

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Forward Noise Adjustment Scheme for Data Augmentation

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Classification of Australian Native Forest Species Using Hyperspectral Remote Sensing and Machine-Learning Classification Algorithms

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FT-MIR and NIR spectral data fusion: a synergetic strategy for the geographical traceability of Panax notoginseng

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An automated framework for QSAR model building

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Spatiotemporal land use random forest model for estimating metropolitan NO2 exposure in Japan

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Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

Loggenberg, K., Strever, A. E., Greyling, B. T., & Poona, N. (2018) · Remote Sensing · doi:10.3390/rs10020202 · Cited by 161

Common, uncommon, and novel applications of random forest in psychological research

Fife, D. A., & D'Onofrio, J. A. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01901-9 · Cited by 173

An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing

Izquierdo‐Verdiguier, E., & Zurita‐Milla, R. (2020) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2020.102051 · Cited by 152

Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection

Dong, Z., Zhang, N., Li, C., Wang, H., Fang, Y., Wang, J., & Zheng, X. (2015) · BMC Cancer · doi:10.1186/s12885-015-1492-6 · Cited by 151

LinkForecast: Cellular Link Bandwidth Prediction in LTE Networks

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Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review

Kuras, A., Brell, M., Rizzi, J., & Burud, I. (2021) · Remote Sensing · doi:10.3390/rs13173393 · Cited by 159

Grouped variable importance with random forests and application to multiple functional data analysis

Gregorutti, B., Michel, B., & Saint-Pierre, P. (2015) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2015.04.002 · Cited by 164

Object-based analysis of unmanned aerial vehicle imagery to map and characterise surface features on a debris-covered glacier

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Application of random forest regression and comparison of its performance to multiple linear regression in modeling groundwater nitrate concentration at the African continent scale

Ouédraogo, I., Defourny, P., & Vanclooster, M. (2018) · Hydrogeology Journal · doi:10.1007/s10040-018-1900-5 · Cited by 147

Machine learning predicting and engineering the yield, N content, and specific surface area of biochar derived from pyrolysis of biomass

Leng, L., Yang, L., Lei, X., Zhang, W., Ai, Z., Yang, Z., Zhan, H., Yang, J., Yuan, X., Peng, H., et al. (2022) · Biochar · doi:10.1007/s42773-022-00183-w · Cited by 148

Cross‐validated permutation feature importance considering correlation between features

Kaneko, H. (2022) · Analytical Science Advances · doi:10.1002/ansa.202200018 · Cited by 144

Tackling concussion in professional rugby union: a case–control study of tackle-based risk factors and recommendations for primary prevention

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Environmental drivers of plant distributions at global and regional scales

Huang, E., Chen, Y., Fang, M., Zheng, Y., & Yu, S. (2021) · Global Ecology and Biogeography · doi:10.1111/geb.13251 · Cited by 142

A Review on Applications of Artificial Intelligence in Wastewater Treatment

Wáng, Y., Cheng, Y., Liu, H., Guo, Q., Dai, C., Zhao, M., & Liu, D. (2023) · Sustainability · doi:10.3390/su151813557 · Cited by 144

Neural Predictors of Initiating Alcohol Use During Adolescence

Squeglia, L. M., Ball, T. M., Jacobus, J., Brumback, T., McKenna, B. S., Nguyen‐Louie, T. T., Sorg, S. F., Paulus, M. P., & Tapert, S. F. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2016.15121587 · Cited by 147

Exploring cultural ecosystem service hotspots: Linking multiple urban forest features with public participation mapping data

Baumeister, C. F., Gerstenberg, T., Plieninger, T., & Schraml, U. (2019) · Urban forestry & urban greening · doi:10.1016/j.ufug.2019.126561 · Cited by 140

A random forest approach for predicting coal spontaneous combustion

Lei, C., Deng, J., Cao, K., Ma, L., Xiao, Y., & Ren, L.-F. (2018) · Fuel · doi:10.1016/j.fuel.2018.03.005 · Cited by 139

AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records

Xie, F., Chakraborty, B., Ong, M. E. H., Goldstein, B. A., & Liu, N. (2020) · JMIR Medical Informatics · doi:10.2196/21798 · Cited by 141

Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images

He, X., Yang, L., Li, A., Zhang, L., Shen, F., Cai, Y., & Zhou, C. (2021) · CATENA · doi:10.1016/j.catena.2021.105442 · Cited by 139

Multi‐Layer Feature Selection Incorporating Weighted Score‐Based Expert Knowledge toward Modeling Materials with Targeted Properties

Liu, Y., Wu, J., Avdeev, M., & Shi, S. (2020) · Advanced Theory and Simulations · doi:10.1002/adts.201900215 · Cited by 140

What Is a Mild Winter? Regional Differences in Within-Species Responses to Climate Change

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Is Romantic Desire Predictable? Machine Learning Applied to Initial Romantic Attraction

Joel, S., Eastwick, P. W., & Finkel, E. J. (2017) · Psychological Science · doi:10.1177/0956797617714580 · Cited by 146

Global Estimation of Biophysical Variables from Google Earth Engine Platform

Campos‐Taberner, M., Moreno‐Martínez, Á., Garcı́a-Haro, F. J., Camps‐Valls, G., Robinson, N., Kattge, J., & Running, S. W. (2018) · Remote Sensing · doi:10.3390/rs10081167 · Cited by 137

Revisiting simulated annealing: A component-based analysis

Franzin, A., & Stützle, T. (2018) · Computers & Operations Research · doi:10.1016/j.cor.2018.12.015 · Cited by 137

A random forest-based model for the prediction of construction-stage carbon emissions at the early design stage

Fang, Y., Lu, X., & Li, H. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.129657 · Cited by 137

Recursive feature elimination in random forest classification supports nanomaterial grouping

Bahl, A., Hellack, B., Balaș, M., Dinischiotu, A., Wiemann, M., Brinkmann, J. F. F., Luch, A., Renard, B. Y., & Haase, A. (2019) · NanoImpact · doi:10.1016/j.impact.2019.100179 · Cited by 140

Deeper waters are changing less consistently than surface waters in a global analysis of 102 lakes

Pilla, R. M., Williamson, C. E., Адамович, Б. В., Adrian, R., Anneville, O., Chandra, S., Colom, W., Devlin, S. P., Dix, M. A., Dokulil, M. T., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-76873-x · Cited by 136

A Systematic Approach for Variable Selection With Random Forests: Achieving Stable Variable Importance Values

Behnamian, A., Millard, K., Banks, S. N., White, L., Richardson, M. C., & Pasher, J. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2745049 · Cited by 135

Feature subset selection Filter–Wrapper based on low quality data

Figueredo, J. M. C., Garrido, M. C., & Martínez‐España, R. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.05.051 · Cited by 137

Predicting CO2 capture of ionic liquids using machine learning

Venkatraman, V., & Alsberg, B. K. (2017) · Journal of CO2 Utilization · doi:10.1016/j.jcou.2017.06.012 · Cited by 135

Mathematical programming for piecewise linear regression analysis

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Identifying appropriate spatial scales of predictors in species distribution models with the random forest algorithm

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Characterizing soil salinity at multiple depth using electromagnetic induction and remote sensing data with random forests: A case study in Tarim River Basin of southern Xinjiang, China

Wang, F., Yang, S., Wei, Y., Shi, Q., & Ding, J. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.142030 · Cited by 134

Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier

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Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach

Liu, Y., Cao, G., Zhao, N., Mulligan, K., & Ye, X. (2018) · Environmental Pollution · doi:10.1016/j.envpol.2017.12.070 · Cited by 132

Auxiliary datasets improve accuracy of object-based land use/land cover classification in heterogeneous savanna landscapes

Hurskainen, P., Adhikari, H., Siljander, M., Pellikka, P., & Hemp, A. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111354 · Cited by 132

Classification of Zambian grasslands using random forest feature importance selection during the optimal phenological period

Zhao, Y., Zhu, W., Wei, P., Fang, P., Zhang, X., Yan, N., Liu, W., Zhao, H., & Wu, Q. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2021.108529 · Cited by 132

Random forests for high-dimensional longitudinal data

Capitaine, L., Genuer, R., & Thiébaut, R. (2020) · Statistical Methods in Medical Research · doi:10.1177/0962280220946080 · Cited by 130

The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China

Zhang, Y., Li, Q., Huang, H., Wu, W., Du, X., & Wang, H. (2017) · Remote Sensing · doi:10.3390/rs9090865 · Cited by 128

A comparison of eight metamodeling techniques for the simulation of N2O fluxes and N leaching from corn crops

Vialaneix, N., Follador, M., Ratto, M., & Leip, A. (2011) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2011.05.003 · Cited by 128

Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models

Ronao, C. A., & Cho, S.-B. (2014) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2014.6975918 · Cited by 126

Urine Proteome Profiling Predicts Lung Cancer from Control Cases and Other Tumors

Zhang, C., Leng, W., Sun, C., Lu, T., Chen, Z., Men, X.-B., Wang, Y., Wang, G., Zhen, B., & Qin, J. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2018.03.009 · Cited by 125

Predicting older people's thermal sensation in building environment through a machine learning approach: Modelling, interpretation, and application

Wang, Z., Yu, H., Luo, M., Wang, Z., Zhang, H., & Jiao, Y. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.106231 · Cited by 129

Developing a Random Forest Algorithm for MODIS Global Burned Area Classification

Ramo, R., & Chuvieco, E. (2017) · Remote Sensing · doi:10.3390/rs9111193 · Cited by 125

Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine

Kang, C., Huo, Y., Xin, L., Tian, B., & Yu, B. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.12.010 · Cited by 128

On the use of Harrell’s C for clinical risk prediction via random survival forests

Schmid, M. C., Wright, M. N., & Ziegler, A. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.07.018 · Cited by 124

Extreme Precipitation in China: A Review on Statistical Methods and Applications

Gu, X., Ye, L., Xin, Q., Zhang, C., Zeng, F., Nerantzaki, S. D., & Papalexiou, S. M. (2022) · Advances in Water Resources · doi:10.1016/j.advwatres.2022.104144 · Cited by 124

Discrimination of Deciduous Tree Species from Time Series of Unmanned Aerial System Imagery

Lisein, J., Michez, A., Claessens, H., & Lejeune, P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141006 · Cited by 123

Spatial prediction of soil water retention in a Páramo landscape: Methodological insight into machine learning using random forest

Blanco, C. M. G., Gómez, V. M. B., Crespo, P., & Ließ, M. (2017) · Geoderma · doi:10.1016/j.geoderma.2017.12.002 · Cited by 130

A demonstration of a multi-method variable selection approach for treatment selection: Recommending cognitive–behavioral versus psychodynamic therapy for mild to moderate adult depression

Cohen, Z. D., Kim, T. T., Van, H. L., Dekker, J., & Driessen, E. (2019) · Psychotherapy Research · doi:10.1080/10503307.2018.1563312 · Cited by 128

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)

Pourghasemi, H. R., Pouyan, S., Heidari, B., Farajzadeh, Z., Shamsi, S. R. F., Babaei, S., Khosravi, R., Etemadi, M., Ghanbarian, G., Farhadi, A., et al. (2020) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2020.06.058 · Cited by 122

An evolutionary deep learning model based on EWKM, random forest algorithm, SSA and BiLSTM for building energy consumption prediction

Lei, L., Shao, S., & Liang, L. (2023) · Energy · doi:10.1016/j.energy.2023.129795 · Cited by 122

Comparison of Machine Learning Algorithms for the Power Consumption Prediction : - Case Study of Tetouan city –

Salam, A., & Hibaoui, A. E. (2018) · International Renewable and Sustainable Energy Conference (IRSEC) · doi:10.1109/irsec.2018.8703007 · Cited by 122

Land cover mapping based on random forest classification of multitemporal spectral and thermal images

Eisavi, V., Homayouni, S., Yazdi, A. M., & Alimohammadi, A. (2015) · Environmental Monitoring and Assessment · doi:10.1007/s10661-015-4489-3 · Cited by 120

Rapidly assessing earthquake-induced landslide susceptibility on a global scale using random forest

He, Q., Wang, M., & Liu, K. (2021) · Geomorphology · doi:10.1016/j.geomorph.2021.107889 · Cited by 120

Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential

Zhou, J., Huang, S., Zhou, T., Armaghani, D. J., & Qiu, Y. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10140-5 · Cited by 120

FTIR spectroscopy of biofluids revisited: an automated approach to spectral biomarker identification

Ollesch, J., Drees, S. L., Heise, H. M., Behrens, T., Brüning, T., & Gerwert, K. B. (2013) · The Analyst · doi:10.1039/c3an00337j · Cited by 119

Machine learning approaches to predict learning outcomes in Massive open online courses

Al-Shabandar, R., Hussain, A. J., Laws, A., Keight, R., Lunn, J., & Radi, N. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7965922 · Cited by 119

Predicting ionic liquid melting points using machine learning

Venkatraman, V., Evjen, S., Knuutila, H. K., Fiksdahl, A., & Alsberg, B. K. (2018) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2018.03.090 · Cited by 118

Automated microscopic image analysis for leukocytes identification: A survey

Saraswat, M., & Arya, K. V. (2014) · Micron · doi:10.1016/j.micron.2014.04.001 · Cited by 117

Variable selection for hedonic model using machine learning approaches: A case study in Onondaga County, NY

Yoo, S., Im, J., & Wagner, J. E. (2012) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2012.06.009 · Cited by 117

A hybrid ensemble method for pulsar candidate classification

Wang, Y., Pan, Z., Zheng, J., Qian, L., & Li, M. (2019) · Astrophysics and Space Science · doi:10.1007/s10509-019-3602-4 · Cited by 117

Random forest methodology for model-based recursive partitioning: the mobForest package for R

Garge, N. R., Bobashev, G., & Eggleston, B. S. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-125 · Cited by 116

More Data or a Better Model? Figuring Out What Matters Most for the Spatial Prediction of Soil Carbon

Somarathna, P. D. S. N., Minasny, B., & Malone, B. (2017) · Soil Science Society of America Journal · doi:10.2136/sssaj2016.11.0376 · Cited by 116

A machine-learning framework for predicting multiple air pollutants' concentrations via multi-target regression and feature selection

Masmoudi, S., Elghazel, H., Taieb, D., Yazar, O., & Kallel, A. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.136991 · Cited by 116

Modeling pedestrian emotion in high-density cities using visual exposure and machine learning: Tracking real-time physiology and psychology in Hong Kong

Xiang, L., Cai, M., Ren, C., & Ng, E. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108273 · Cited by 120

Single-Subject Anxiety Treatment Outcome Prediction using Functional Neuroimaging

Ball, T. M., Stein, M. B., Ramsawh, H. J., Campbell‐Sills, L., & Paulus, M. P. (2013) · Neuropsychopharmacology · doi:10.1038/npp.2013.328 · Cited by 120

Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features

Zheng, H., Du, P., Chen, J., Xia, J., Li, E., Xu, Z., Li, X., & Yokoya, N. (2017) · Remote Sensing · doi:10.3390/rs9121274 · Cited by 115

Untangling fine-grained code changes

Dias, M., Bacchelli, A., Gousios, G., Cassou, D., & Ducasse, S. (2015) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner.2015.7081844 · Cited by 114

Unbiased Feature Selection in Learning Random Forests for High‐Dimensional Data

Nguyen, T.-T., Huang, J. Z., & Thủy, N. T. T. (2015) · The Scientific World JOURNAL · doi:10.1155/2015/471371 · Cited by 113

Toward evidence-based learning analytics: Using proxy variables to improve asynchronous online discussion environments

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An Efficient Android Malware Detection System Based on Method-Level Behavioral Semantic Analysis

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Machine-learning-assisted shear strength prediction of reinforced concrete beams with and without stirrups

Zhang, J., Sun, Y., Li, G., Wang, Y., Sun, J., & Li, J. (2020) · Engineering With Computers · doi:10.1007/s00366-020-01076-x · Cited by 111

Investigating impacts of asphalt mixture properties on pavement performance using LTPP data through random forests

Gong, H., Sun, Y., Hu, W., Polaczyk, P. A., & Huang, B. (2019) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2019.01.198 · Cited by 110

A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility

Arabameri, A., Saha, S., Roy, J., Tiefenbacher, J. P., Cerdà, A., Biggs, T. W., Pradhan, B., Ngo, P. T. T., & Collins, A. L. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.138595 · Cited by 110

Predicting coastal algal blooms with environmental factors by machine learning methods

Yu, P., Gao, R., Zhang, D., & Liu, Z. (2021) · Ecological Indicators · doi:10.1016/j.ecolind.2020.107334 · Cited by 111

A hybrid KMV model, random forests and rough set theory approach for credit rating

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Landsat-based approaches for mapping of land degradation prevalence and soil functional properties in Ethiopia

Vågen, T.-G., Winowiecki, L. A., Abegaz, A., & Hadgu, K. M. (2013) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.03.006 · Cited by 109

Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

Ng, W., Mbaabu, P. R., Einzmann, K., Immitzer, M., Atzberger, C., & Eckert, S. (2017) · Remote Sensing · doi:10.3390/rs9010074 · Cited by 109

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