Variable selection using random forests

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

The Elements of Statistical Learning

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162

Classification and Regression Trees.

Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867

Classification and Regression Trees.

Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030

Classification and Regression by randomForest

Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594

Bagging predictors

Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325

Principal Component Analysis

Jolliffe, I. T. (2005) · Wiley eBooks · doi:10.1002/0470013192.bsa501 · Cited by 14,522

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

Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999) · Science · doi:10.1126/science.286.5439.531 · Cited by 11,516

Gene selection for cancer classification using support vector machines

Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954

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

Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al. (2000) · Nature · doi:10.1038/35000501 · Cited by 9,925

Responses of an endangered brown bear population to climate change based on predictable food resource and shelter alterations

Penteriani, V., Zarzo‐Arias, A., Novo‐Fernández, A., Bombieri, G., & López-Sánchez, C. A. (2019) · Global Change Biology · doi:10.1111/gcb.14564 · Cited by 108

Multi-scale habitat modelling and predicting change in the distribution of tiger and leopard using random forest algorithm

Rather, T. A., Kumar, S., & Khan, J. A. (2020) · Scientific Reports · doi:10.1038/s41598-020-68167-z · Cited by 108

A Surrogate-Assisted Evolutionary Feature Selection Algorithm With Parallel Random Grouping for High-Dimensional Classification

Liu, S., Wang, H., Peng, W., & Yao, W. (2022) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2022.3149601 · Cited by 104

Attribute clustering using rough set theory for feature selection in fault severity classification of rotating machinery

Pacheco, F., Cerrada, M., Sánchez, R., Cabrera, D. R., Li, C., & de Oliveira, J. V. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.11.024 · Cited by 103

Assessment of Sentinel-2 MSI Spectral Band Reflectances for Estimating Fractional Vegetation Cover

Wang, B., Jia, K., Liang, S., Xie, X., Wei, X., Zhao, X. H., Yao, Y., & Zhang, X. (2018) · Remote Sensing · doi:10.3390/rs10121927 · Cited by 103

High resolution population estimates from telecommunications data

Douglass, R. W., MEYER, D. A., Ram, M., Rideout, D., & Song, D. (2015) · EPJ Data Science · doi:10.1140/epjds/s13688-015-0040-6 · Cited by 102

Poaching empties critical Central African wilderness of forest elephants

Poulsen, J. R., Koerner, S. E., Moore, S., Medjibe, V. P., Blake, S., Clark, C. J., Akou, M. E., Fay, M., Meier, A. C., Okouyi, J., et al. (2017) · Current Biology · doi:10.1016/j.cub.2017.01.023 · Cited by 105

Prediction & optimization of alkali-activated concrete based on the random forest machine learning algorithm

Sun, Y., Cheng, H., Zhang, S., Mohan, M. K., Ye, G., & Schutter, G. D. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.131519 · Cited by 102

Mapping Monthly Air Temperature in the Tibetan Plateau From MODIS Data Based on Machine Learning Methods

Xu, Y., Knudby, A. J., Shen, Y., & Liu, Y. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2787191 · Cited by 101

Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations

Maxwell, A. E., Strager, M. P., Warner, T. A., Ramezan, C. A., Morgan, A. N., & Pauley, C. E. (2019) · Remote Sensing · doi:10.3390/rs11121409 · Cited by 101

Large Area Mapping of Annual Land Cover Dynamics Using Multitemporal Change Detection and Classification of Landsat Time Series Data

Franklin, S. E., Ahmed, O. S., Wulder, M. A., White, J. C., Hermosilla, T., & Coops, N. C. (2015) · Canadian Journal of Remote Sensing · doi:10.1080/07038992.2015.1089401 · Cited by 100

Autoantibody‐Positive Healthy Individuals Display Unique Immune Profiles That May Regulate Autoimmunity

Slight-Webb, S. R., Lu, R., Ritterhouse, L. L., Munroe, M. E., Maecker, H. T., Fathman, C. G., Utz, P. J., Merrill, J. T., Guthridge, J. M., & James, J. A. (2016) · Arthritis & Rheumatology · doi:10.1002/art.39706 · Cited by 100

Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data

Ruescas, A. B., Hieronymi, M., Mateo‐García, G., Koponen, S., Kallio, K. Y., & Camps‐Valls, G. (2018) · Remote Sensing · doi:10.3390/rs10050786 · Cited by 100

Mean decrease accuracy for random forests: inconsistency, and a practical solution via the Sobol-MDA

Bénard, C., da Veiga, S., & Scornet, E. (2022) · Biometrika · doi:10.1093/biomet/asac017 · Cited by 111

Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification

Rahman, Q. A., Tereshchenko, L. G., Kongkatong, M., Abraham, T. P., Abraham, M. R., & Shatkay, H. (2015) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2015.2426213 · Cited by 103

Repeated sleep disruption in mice leads to persistent shifts in the fecal microbiome and metabolome

Bowers, S. J., Vargas, F., González, A., He, S., Jiang, P., Dorrestein, P. C., Knight, R. D., Wright, K. P., Lowry, C. A., Fleshner, M., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0229001 · Cited by 99

Geographic Object-Based Image Analysis: A Primer and Future Directions

Kucharczyk, M., Hay, G. J., Ghaffarian, S., & Hugenholtz, C. H. (2020) · Remote Sensing · doi:10.3390/rs12122012 · Cited by 111

Water clarity in Brazilian water assessed using Sentinel-2 and machine learning methods

Maciel, D. A., Barbosa, C. C. F., Novo, E. M. L. D. M., Júnior, R. F., & Begliomini, F. N. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.10.009 · Cited by 103

DRQ: Dynamic Region-based Quantization for Deep Neural Network Acceleration

Song, Z., Fu, B., Wu, F., Jiang, Z., Jiang, L., Jing, N., & Liang, X. (2020) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca45697.2020.00086 · Cited by 102

Simulation of seepage flow through embankment dam by using a novel extended Kalman filter based neural network paradigm: Case study of Fontaine Gazelles Dam, Algeria

Rehamnia, I., Benlaoukli, B., Jamei, M., Karbasi, M., & Malik, A. (2021) · Measurement · doi:10.1016/j.measurement.2021.109219 · Cited by 98

XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection

Faysal, J. A., Mostafa, S. T., Tamanna, J. S., Mumenin, K. M., Arifin, M. M., Awal, M. A., Shome, A., & Mostafa, S. S. (2022) · Telecom · doi:10.3390/telecom3010003 · Cited by 98

Cotton Classification Method at the County Scale Based on Multi-Features and Random Forest Feature Selection Algorithm and Classifier

Fei, H., Fan, Z., Wang, C., Zhang, N., Wang, T., Chen, R., & Bai, T. (2022) · Remote Sensing · doi:10.3390/rs14040829 · Cited by 98

Random forest–based estimation of heavy metal concentration in agricultural soils with hyperspectral sensor data

Tan, K., Ma, W., Wu, F., & Du, Q. (2019) · Environmental Monitoring and Assessment · doi:10.1007/s10661-019-7510-4 · Cited by 97

Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness

Li, J., Tran, M., & Siwabessy, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149089 · Cited by 96

Delimiting floristic biogeographic districts in the Cerrado and assessing their conservation status

Françoso, R. D., Dexter, K. G., Machado, R. B., Pennington, R. T., Pinto, J. R. R., Brandão, R. A., & Ratter, J. A. (2019) · Biodiversity and Conservation · doi:10.1007/s10531-019-01819-3 · Cited by 108

Opportunities drive the global distribution of protected areas

Baldi, G., Texeira, M. A., Martin, O. A., Grau, H. R., & Jobbagy, E. G. (2017) · PeerJ · doi:10.7717/peerj.2989 · Cited by 100

Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models

Ronao, C. A., & Cho, S.-B. (2017) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147716683687 · Cited by 95

Random forests: A machine learning methodology to highlight the volatile organic compounds involved in olfactory perception

Vigneau, É., Courcoux, P., Symoneaux, R., Guérin, L., & Villière, A. (2018) · Food Quality and Preference · doi:10.1016/j.foodqual.2018.02.008 · Cited by 95

Daily Global Solar Radiation in China Estimated From High‐Density Meteorological Observations: A Random Forest Model Framework

Zeng, Z., Wang, Z., Gui, K., Yan, X., Gao, M. C., Luo, M., Geng, H., Liao, T., Li, X., An, J., et al. (2020) · Earth and Space Science · doi:10.1029/2019ea001058 · Cited by 95

Prediction of material removal rate in chemical mechanical polishing via residual convolutional neural network

Zhang, J., Jiang, Y., Luo, H., & Yin, S. (2020) · Control Engineering Practice · doi:10.1016/j.conengprac.2020.104673 · Cited by 95

Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions?

Stell, E., Warner, D. L., Jian, J., Bond‐Lamberty, B., & Vargas, R. (2021) · Global Change Biology · doi:10.1111/gcb.15666 · Cited by 95

Modeling Anthropogenic Fire Occurrence in the Boreal Forest of China Using Logistic Regression and Random Forests

Guo, F., Zhang, L., Jin, S., Tigabu, M., Su, Z., & Wang, W. (2016) · Forests · doi:10.3390/f7110250 · Cited by 94

A machine learning framework to trace tumor tissue-of-origin of 13 types of cancer based on DNA somatic mutation

He, B., Dai, C., Lang, J., Bing, P., Tian, G., Wang, B., & Yang, J. (2020) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2020.165916 · Cited by 94

Northwest Forest Plan–the first 20 years (1994-2013): status and trends of late-successional and old-growth forests

Davis, R. J., Ohmann, J. L., Kennedy, R. E., Cohen, W. B., Gregory, M. J., Yang, Z., Roberts, H. M., Gray, A. N., & Spies, T. A. (2015) · doi:10.2737/pnw-gtr-911 · Cited by 93

Improving Service Availability of Cloud Systems by Predicting Disk Error.

Xu, Y. D., Sui, K., Yao, R., Zhang, H., Lin, Q., Dang, Y., Li, P., Jiang, K., Zhang, W., Lou, J., et al. (2018) · Figshare · Cited by 92

Occurrence prediction of cotton pests and diseases by bidirectional long short-term memory networks with climate and atmosphere circulation

Chen, P., Xiao, Q., Zhang, J., Xie, C., & Wang, B. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105612 · Cited by 93

LCZ scheme for assessing Urban Heat Island intensity in a complex urban area (Beirut, Lebanon)

Badaro-Saliba, N., Adjizian-Gérard, J., Zaarour, R., & Najjar, G. (2021) · Urban Climate · doi:10.1016/j.uclim.2021.100846 · Cited by 92

It's in your blood: spectral biomarker candidates for urinary bladder cancer from automated FTIR spectroscopy

Ollesch, J., Heinze, M., Heise, H. M., Behrens, T., Brüning, T., & Gerwert, K. B. (2014) · Journal of Biophotonics · doi:10.1002/jbio.201300163 · Cited by 93

Proximal support vector machine based hybrid prediction models for trend forecasting in financial markets

Kumar, D., Meghwani, S. S., & Thakur, M. (2016) · Journal of Computational Science · doi:10.1016/j.jocs.2016.07.006 · Cited by 92

A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones

Fenta, H. M., Zewotir, T. T., & Muluneh, E. K. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01652-1 · Cited by 98

Feature engineering for improved machine-learning-aided studying heavy metal adsorption on biochar

Shen, T., Peng, H., Yuan, X., Liang, Y., Liu, S., Wu, Z., Leng, L., & Qin, P. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.133442 · Cited by 92

Biodiversity Mapping in a Tropical West African Forest with Airborne Hyperspectral Data

Laurin, G. V., Chan, J. C.-W., Chen, Q., Lindsell, J., Coomes, D. A., Guerriero, L., Frate, F. D., Miglietta, F., & Valentini, R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097910 · Cited by 91

Efficacy of species distribution models (SDMs) for ecological realms to ascertain biological conservation and practices

Rathore, M. K., & Sharma, L. K. (2023) · Biodiversity and Conservation · doi:10.1007/s10531-023-02648-1 · Cited by 98

Sequential random k-nearest neighbor feature selection for high-dimensional data

Park, C. H., & Kim, S. B. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.10.044 · Cited by 90

Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

Cerrada, M., Sánchez, R., Cabrera, D. R., Zurita, G., & Li, C. (2015) · Sensors · doi:10.3390/s150923903 · Cited by 90

Variance reduction in purely random forests

Genuer, R. (2012) · Journal of nonparametric statistics · doi:10.1080/10485252.2012.677843 · Cited by 89

Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches

Delerce, S., Dorado, H., Grillon, A., Rebolledo, M. C., Prager, S. D., Patiño, V. H., Garcés-Varón, G., & Jimenez, D. R. (2016) · PLoS ONE · doi:10.1371/journal.pone.0161620 · Cited by 98

Forest-Based and Semiparametric Methods for the Postprocessing of Rainfall Ensemble Forecasting

Taillardat, M., Fougères, A.-L., Naveau, P., & Mestre, O. (2017) · Weather and forecasting · doi:10.1175/waf-d-18-0149.1 · Cited by 63

Covariate selection strategies for causal inference: Classification and comparison

Witte, J., & Didelez, V. (2018) · Biometrical Journal · doi:10.1002/bimj.201700294 · Cited by 88

The Performance of Random Forest Classification Based on Phenological Metrics Derived from Sentinel-2 and Landsat 8 to Map Crop Cover in an Irrigated Semi-arid Region

Htitiou, A., Boudhar, A., Lebrini, Y., Hadria, R., Lionboui, H., Mansouri, L. E., Tychon, B., & Benabdelouahab, T. (2019) · Remote Sensing in Earth Systems Sciences · doi:10.1007/s41976-019-00023-9 · Cited by 88

Metrics of Lidar-Derived 3D Vegetation Structure Reveal Contrasting Effects of Horizontal and Vertical Forest Heterogeneity on Bird Species Richness

Carrasco, L., Giam, X., Papeş, M., & Sheldon, K. S. (2019) · Remote Sensing · doi:10.3390/rs11070743 · Cited by 87

A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon

Santos, F., Graw, V., & Bonilla‐Bedoya, S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0226224 · Cited by 87

Improving healthcare operations management with machine learning

Pianykh, O. S., Guitron, S., Parke, D., Zhang, C., Pandharipande, P., Brink, J., & Rosenthal, D. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0176-3 · Cited by 87

Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification

Dong, L., Li, X., & Xie, G. (2014) · Abstract and Applied Analysis · doi:10.1155/2014/459137 · Cited by 85

Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability

Uddin, M. S., Chi, G., Janabi, M. A. M. A., & Habib, T. (2020) · International Journal of Finance & Economics · doi:10.1002/ijfe.2346 · Cited by 85

Delayed response of groundwater to multi-year meteorological droughts in the absence of anthropogenic management

Schreiner‐McGraw, A. P., & Ajami, H. (2021) · Journal of Hydrology · doi:10.1016/j.jhydrol.2021.126917 · Cited by 85

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

Rahmati, O., Ghorbanzadeh, O., Teimurian, T., Mohammadi, F., Tiefenbacher, J. P., Falah, F., Pirasteh, S., Ngo, P. T. T., & Bui, D. T. (2019) · Remote Sensing · doi:10.3390/rs11242995 · Cited by 84

(Q)SAR modelling of nanomaterial toxicity: A critical review

Karakuş, C. Ö., Yue, C. X., Liu, J. J., Wilkins, T., & Wang, X. Z. (2015) · Particuology · doi:10.1016/j.partic.2014.12.001 · Cited by 85

Machine Learning Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data

Marrs, J. K., & Ni‐Meister, W. (2019) · Remote Sensing · doi:10.3390/rs11070819 · Cited by 84

Evaluation of random forest and regression tree methods for estimation of mass first flush ratio in urban catchments

Jeung, M., Baek, S., Beom, J., Cho, K. H., Her, Y., & Yoon, K. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.05.079 · Cited by 83

Explaining relationships between coke quality index and coal properties by Random Forest method

Chelgani, S. C., Matin, S. S., & Hower, J. C. (2016) · Fuel · doi:10.1016/j.fuel.2016.06.034 · Cited by 83

Introducing a system of wind speed distributions for modeling properties of wind speed regimes around the world

Jung, C., Schindler, D., Laible, J., & Buchholz, A. (2017) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.04.044 · Cited by 83

Symmetrical uncertainty and random forest for the evaluation of gridded precipitation and temperature data

Nashwan, M. S., & Shahid, S. (2019) · Atmospheric Research · doi:10.1016/j.atmosres.2019.104632 · Cited by 82

Wind Turbine Pitch System Condition Monitoring and Fault Detection Based on Optimized Relevance Vector Machine Regression

Wei, L., Qian, Z., & Zareipour, H. (2019) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2019.2954834 · Cited by 82

Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms

Mallick, J., Alqadhi, S. D., Talukdar, S., Alsubih, M., Ahmed, M., Khan, R. A., Kahla, N. B., & Abutayeh, S. M. (2021) · Sustainability · doi:10.3390/su13020457 · Cited by 82

Fingerprinting black tea: When spectroscopy meets machine learning a novel workflow for geographical origin identification

Li, Y. F., Logan, N., Quinn, B., Hong, Y., Birse, N., Zhu, H., Haughey, S. A., Elliott, C. T., & Wu, D. (2023) · Food Chemistry · doi:10.1016/j.foodchem.2023.138029 · Cited by 82

Random forest techniques for spatial interpolation of evapotranspiration data from Brazilian’s Northeast

Júnior, J. C. D. S., de Medeiros, V. W. C., Garrozi, C., Montenegro, A. A. D. A., & Gonçalves, G. E. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.105017 · Cited by 81

Combining Multiple Feature-Ranking Techniques and Clustering of Variables for Feature Selection

Haq, A. U., Zhang, D., Peng, H., & Rahman, S. U. (2019) · IEEE Access · doi:10.1109/access.2019.2947701 · Cited by 87

Soil respiration strongly offsets carbon uptake in Alaska and Northwest Canada

Watts, J. D., Natali, S. M., Minions, C., Risk, D. A., Arndt, K. A., Zona, D., Euskirchen, E. S., Rocha, A. V., Sonnentag, O., Helbig, M., et al. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/ac1222 · Cited by 81

Random Forests Unsupervised Classification: The Detection and Mapping of Solanum mauritianum Infestations in Plantation Forestry Using Hyperspectral Data

Peerbhay, K. Y., Mutanga, O., & Ismail, R. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2396577 · Cited by 81

Assessing the general patterns of forest structure: quantifying tree and forest allometric scaling relationships in the U nited S tates

Duncanson, L. I., Dubayah, R., & Enquist, B. J. (2015) · Global Ecology and Biogeography · doi:10.1111/geb.12371 · Cited by 81

Estimating Mediterranean forest parameters using multi seasonal Landsat 8 OLI imagery and an ensemble learning method

Chrysafis, I., Mallinis, G., Gitas, I. Z., & Tsakiri–Strati, M. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.07.018 · Cited by 82

Machine Learning Applications for Earth Observation

Lary, D. J., Zewdie, G. K., Liu, X., Wu, D., Levetin, E., Allee, R. J., Malakar, N. K., Walker, A. L., Mussa, H. Y., Mannino, A., et al. (2018) · Springer eBooks · doi:10.1007/978-3-319-65633-5_8 · Cited by 87

Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error

Beheshti, I., Demirel, H., Farokhian, F., Yang, C., & Matsuda, H. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.09.019 · Cited by 80

Detection of impact on aircraft composite structure using machine learning techniques

Ai, L. M., Soltangharaei, V., Bayat, M., van Tooren, M. J. L., & Ziehl, P. H. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/abe790 · Cited by 80

Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis

Tun, W., Wong, J. K. W., & Ling, S. H. (2021) · Sensors · doi:10.3390/s21248163 · Cited by 80

Earth Observation Open Science and Innovation

Mathieu, P.-P., & Aubrecht, C. (2018) · Springer eBooks · doi:10.1007/978-3-319-65633-5 · Cited by 82

Soil nitrogen and climate drive the positive effect of biological soil crusts on soil organic carbon sequestration in drylands: A Meta-analysis

Xu, H., Zhang, Y., Shao, X., & Liu, N. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.150030 · Cited by 79

Mapping China’s Forest Fire Risks with Machine Learning

Shao, Y., Feng, Z., Sun, L., Yang, X., Li, Y., Xu, B., & Chen, Y. (2022) · Forests · doi:10.3390/f13060856 · Cited by 79

Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection

Liu, N., Koh, Z. X., Goh, K. J., Lin, Z., Haaland, B. A., Ting, B. P., & Ong, M. E. H. (2014) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-14-75 · Cited by 79

Random forests for feature selection in QSPR Models - an application for predicting standard enthalpy of formation of hydrocarbons

Teixeira, A. L., Leal, J. P., & Falcão, A. O. (2013) · Journal of Cheminformatics · doi:10.1186/1758-2946-5-9 · Cited by 81

Risky business: The impact of climate and climate variability on human population dynamics in Western Europe during the Last Glacial Maximum

Burke, A. M., Kageyama, M., Latombe, G., Fasel, M., Vrac, M., Ramstein, G., & James, P. M. A. (2017) · Quaternary Science Reviews · doi:10.1016/j.quascirev.2017.04.001 · Cited by 85

Welding penetration prediction with passive vision system

Chen, Z., Chen, J., & Feng, Z. (2018) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2018.10.009 · Cited by 78

Predicting ground-level PM2.5 concentrations in the Beijing-Tianjin-Hebei region: A hybrid remote sensing and machine learning approach

Li, X., & Zhang, X. (2019) · Environmental Pollution · doi:10.1016/j.envpol.2019.03.068 · Cited by 78

Identification of Leaf-Scale Wheat Powdery Mildew (Blumeria graminis f. sp. Tritici) Combining Hyperspectral Imaging and an SVM Classifier

Zhao, J., Yan, F., Chu, G., Yan, H., Hu, L., & Huang, L. (2020) · Plants · doi:10.3390/plants9080936 · Cited by 79

Large-scale crop mapping from multi-source optical satellite imageries using machine learning with discrete grids

Yan, S., Yao, X., Zhu, D., Liu, D., Zhang, L., Yu, G., Gao, B., Yang, J., & Yun, W. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102485 · Cited by 78

Assessing the social context of wildfire-affected areas. The case of mainland Portugal

Oliveira, S., Zêzere, J. L., Queirós, M., & Pereira, J. M. C. (2017) · Applied Geography · doi:10.1016/j.apgeog.2017.09.004 · Cited by 78

Comparison of simulated hyperspectral HyspIRI and multispectral Landsat 8 and Sentinel-2 imagery for multi-seasonal, regional land-cover mapping

Clark, M. L. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.08.028 · Cited by 77

Balancing prediction accuracy and generalization ability: A hybrid framework for modelling the annual dynamics of satellite-derived land surface temperatures

Liu, Z., Zhan, W., Lai, J., Hong, F., Quan, J., Bechtel, B., Huang, F., & Zou, Z. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.03.013 · Cited by 77

An ensemble method to improve prediction of earthquake-induced soil liquefaction: a multi-dataset study

Zhang, J., & Wang, Y. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05084-2 · Cited by 77

UAV-Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions

Chivasa, W., Mutanga, O., & Biradar, Ç. (2020) · Remote Sensing · doi:10.3390/rs12152445 · Cited by 77

Machine Learning in Chemistry

Janet, J. P., & Kulik, H. J. (2020) · ACS in focus · doi:10.1021/acs.infocus.7e4001 · Cited by 81

A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations

Sikorska‐Senoner, A. E., & Quilty, J. (2021) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2021.105094 · Cited by 77

Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys

Waser‬, L. T., Ginzler, C., & Rehush, N. (2017) · Remote Sensing · doi:10.3390/rs9080766 · Cited by 77

Machine Learning Approach to Delineate the Impact of Material Properties on Solar Cell Device Physics

Islam, M. S., Islam, M. T., Sarker, S., Jame, H. A., Nishat, S. S., Jani, M. R., Rauf, A., Ahsan, S., Shorowordi, K. M., Efstathiadis, H., et al. (2022) · ACS Omega · doi:10.1021/acsomega.2c01076 · Cited by 76

Performance of Random Forest and SVM in Face Recognition

Kremić, E., & Subaşı, A. (2016) · The International Arab Journal of Information Technology · Cited by 76

Development of shale gas production prediction models based on machine learning using early data

Niu, W., Lu, J., & Sun, Y. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.12.040 · Cited by 76

Toward Probabilistic Prediction of Flash Flood Human Impacts

Terti, G., Ruin, I., Gourley, J. J., Kirstetter, P., Flamig, Z. L., Blanchet, J., Arthur, A., & Anquetin, S. (2017) · Risk Analysis · doi:10.1111/risa.12921 · Cited by 76

Exploiting investors social network for stock prediction in China's market

Zhang, X., Shi, J., Wang, D., & Fang, B. (2017) · Journal of Computational Science · doi:10.1016/j.jocs.2017.10.013 · Cited by 78

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