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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New insights into nitrogen removal potential in urban river by revealing the importance of microbial community succession on suspended particulate matter

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Plasma profiling reveals a blood-based metabolic fingerprint of obstructive sleep apnea

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A comparative study of combining tree‐based feature selection methods and classifiers in personal loan default prediction

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Identification and mapping of soybean and maize crops based on Sentinel-2 data

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Application of machine learning in the diagnosis of vestibular disease

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Estimator selection with respect to Hellinger-type risks

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Bayesian neural adjustment of inhibitory control predicts emergence of problem stimulant use

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Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity

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RERT: A Novel Regression Tree Approach to Predict Extrauterine Disease in Endometrial Carcinoma Patients

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Respiratory Microbiota Predicts Clinical Disease Course of Acute Otorrhea in Children With Tympanostomy Tubes

Man, W. H., van Dongen, T. M. A., Venekamp, R. P., Pluimakers, V. G., Chu, M. L. J. N., van Houten, M. A., Sanders, E. A. M., Schilder, A. G., & Bogaert, D. (2018) · The Pediatric Infectious Disease Journal · doi:10.1097/inf.0000000000002215 · Cited by 28

Personalized Effect of Health Behavior on Blood Pressure: Machine Learning Based Prediction and Recommendation

Chiang, P.-H., & Dey, S. (2018) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2018.8531109 · Cited by 27

Sign-Consistency Based Variable Importance for Machine Learning in Brain Imaging

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A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm

Khaled, A. Y., Aziz, S. A., Bejo, S. K., Nawi, N. M., Jamaludin, D., & Ibrahim, N. U. A. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105288 · Cited by 27

Year-to-year crown condition poorly contributes to ring width variations of beech trees in French ICP level I network

Tallieu, C., Badeau, V., Allard, D., Nageleisen, L.-M., & Bréda, N. (2020) · Forest Ecology and Management · doi:10.1016/j.foreco.2020.118071 · Cited by 27

Variable selection and importance in presence of high collinearity: an application to the prediction of lean body mass from multi-frequency bioelectrical impedance

Cammarota, C., & Pinto, A. (2020) · Journal of Applied Statistics · doi:10.1080/02664763.2020.1763930 · Cited by 27

Identifying and understanding determinants of high healthcare costs for breast cancer: a quantile regression machine learning approach

Hu, L., Li, L., Ji, J., & Sanderson, M. F. (2020) · BMC Health Services Research · doi:10.1186/s12913-020-05936-6 · Cited by 27

Smart Cities-Based Improving Atmospheric Particulate Matters Prediction Using Chi-Square Feature Selection Methods by Employing Machine Learning Techniques

Mengash, H. A., Hussain, L., Mahgoub, H., Alqarafi, A., Nour, M. K., Marzouk, R., Qureshi, S. A., & Hilal, A. M. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2022.2067647 · Cited by 27

CrowdAdvisor: A Framework for Freelancer Assessment in Online Marketplace

Abhinav, K., Dubey, A., Jain, S., Virdi, G., Kass, A., & Mehta, M. (2017) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip.2017.23 · Cited by 26

ATEN: And/Or tree ensemble for inferring accurate Boolean network topology and dynamics

Shi, N., Zhu, Z., Tang, K., Parker, D., & He, S. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz563 · Cited by 27

Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests

Vantas, K., Sidiropoulos, E. G., & Loukas, A. G. (2020) · Water · doi:10.3390/w12030687 · Cited by 26

Cross-Project Vulnerability Prediction Based on Software Metrics and Deep Learning

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The potential for using rare, native species in reforestation– A case study of yews (Taxaceae) in China

Jensen, D. A., Rao, M., Zhang, J., Grøn, M., Tian, S., Ma, K., & Svenning, J. (2020) · Forest Ecology and Management · doi:10.1016/j.foreco.2020.118816 · Cited by 28

Microclimates hold the key to spatial forest planning under climate change: Cyanolichens in temperate rainforest

Ellis, C. J., & Eaton, S. (2021) · Global Change Biology · doi:10.1111/gcb.15514 · Cited by 27

Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates

Siegel, C., LASKA, E. M., Lin, Z., Xu, M., Amara, D. A., Jeffers, M. K., Qian, M., Milton, N., Flory, J. D., Hammamieh, R., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01324-8 · Cited by 26

Comfort temperature prediction according to an adaptive approach for educational buildings in tropical climate using artificial neural networks

López-Pérez, L. A., Flores-Prieto, J. J., & Rios-Rojas, C. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111328 · Cited by 26

Comparative Evaluation of Different Targeted and Untargeted Analytical Approaches to Assess Greek Extra Virgin Olive Oil Quality and Authentication

Drakopoulou, S. K., Orfanakis, E., Karagiannaki, I., Gaitis, F., Skoulika, S., Papaioannou, A., Boukouvalas, G., Petropoulos, G. P., Katsoudas, V., Kontzedaki, R., et al. (2022) · Molecules · doi:10.3390/molecules27041350 · Cited by 26

A babel of web-searches: Googling unemployment during the pandemic

Caperna, G., Colagrossi, M., Geraci, A., & Mazzarella, G. (2020) · Labour Economics · doi:10.1016/j.labeco.2021.102097 · Cited by 26

Prediction of Total Phosphorus Concentration in Macrophytic Lakes Using Chlorophyll-Sensitive Bands: A Case Study of Lake Baiyangdian

Zhang, L., Zhang, L., Cen, Y., Wang, S., Zhang, Y., Huang, Y. T., Sultan, M., & Tong, Q. (2022) · Remote Sensing · doi:10.3390/rs14133077 · Cited by 26

Metabolic and inflammatory profiles define phenotypes with clinical relevance in female knee osteoarthritis patients with joint effusion

Calvet, J., García-Manrique, M., Berenguer, A., Orellana, C., Garcia-Cirera, S., Llop-Vilaltella, M., Galisteo, C., Arévalo, M., Aymerich, C., Gómez, R., et al. (2023) · Rheumatology · doi:10.1093/rheumatology/kead135 · Cited by 26

Airborne laser scanning and spectral remote sensing give a bird's eye perspective on arctic tundra breeding habitat at multiple spatial scales

Boelman, N. T., Holbrook, J. D., Greaves, H. E., Krause, J. S., Chmura, H. E., Magney, T. S., Pérez, J. H., Eitel, J. U. H., Gough, L., Vierling, K. T., et al. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.07.012 · Cited by 26

Incorporating association rule networks in feature category-weighted naive Bayes model to support weaning decision making

Gao, Y., Xu, A., Hu, P. J.-H., & Cheng, T.-H. (2017) · Decision Support Systems · doi:10.1016/j.dss.2017.01.007 · Cited by 26

What Predicts Stroop Performance? A Conditional Random Forest ApproachPreprint

IJzerman, H., Pollet, T. V., & Ebersole, C. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2805205 · Cited by 28

An analysis of ensemble pruning methods under the explanation of Random Forest

Khalifa, F. A., Abdelkader, H. M., & Elsaid, A. H. (2023) · Information Systems · doi:10.1016/j.is.2023.102310 · Cited by 25

Canopy temperature dynamics are closely aligned with ecosystem water availability across a water- to energy-limited gradient

Javadian, M., Scott, R. L., Woodgate, W., Richardson, A. D., Dannenberg, M. P., & Smith, W. K. (2024) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2024.110206 · Cited by 25

Predicting river bed substrate cover proportions across New Zealand

Haddadchi, A., Booker, D. J., & Measures, R. J. (2017) · CATENA · doi:10.1016/j.catena.2017.12.014 · Cited by 25

An integrated method based on refined composite multivariate hierarchical permutation entropy and random forest and its application in rotating machinery

Wang, X., Si, S., Li, Y., & Du, X. (2019) · Journal of Vibration and Control · doi:10.1177/1077546319877711 · Cited by 25

Sensor placement methodology for spindle thermal compensation of machine tools

Tsai, P.-C., Cheng, C.-C., Chen, W.-J., & Su, S.-J. (2020) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-04932-8 · Cited by 25

Classification of oil palm female inflorescences anthesis stages using machine learning approaches

Mamehgol Yousefi, D. B., Rafie, A. S. M., Aziz, S. A., Azrad, S., Masri, M. M. M., Shahi, A., & Marzuki, O. F. (2020) · Information Processing in Agriculture · doi:10.1016/j.inpa.2020.11.007 · Cited by 25

Statistical estimation of next-day nighttime surface urban heat islands

Lai, J., Zhan, W., Quan, J., Bechtel, B., Wang, K., Zhou, J. P., Huang, F., Chakraborty, T., Liu, Z., & Lee, X. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.04.009 · Cited by 25

A Random Forest Model for the Prediction of FOG Content in Inlet Wastewater from Urban WWTPs

Pérez, V. M., Fernández, J. M. M., Balsera, J. M. V., & Álvarez, C. A. (2021) · Water · doi:10.3390/w13091237 · Cited by 25

ADASYN-LOF Algorithm for Imbalanced Tornado Samples

Qing, Z., Zeng, Q., Wang, H., Liu, Y., Xiong, T., & Zhang, S. (2022) · Atmosphere · doi:10.3390/atmos13040544 · Cited by 27

A machine learning approach to predict the structural and magnetic properties of Heusler alloy families

Mitra, S., Ahmad, A., Biswas, S., & Das, A. K. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111836 · Cited by 25

Predictive performance of random forest on the identification of mangrove species in arid environments

Aviña-Hernández, J., Ramírez-Vargas, M., Roque-Sosa, F., & Martínez‐Rincón, R. O. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102040 · Cited by 25

Development of prediction models for interlayer shear strength in asphalt pavement using machine learning and SHAP techniques

AL-Jarazi, R. S., Rahman, A., Ai, C., Al‐Huda, Z., & Ariouat, H. (2023) · Road Materials and Pavement Design · doi:10.1080/14680629.2023.2276412 · Cited by 25

Genetic justification of COVID ‐19 patient outcomes using DERGA , a novel data ensemble refinement greedy algorithm

Asteris, P. G., Gandomi, A. H., Armaghani, D. J., Tsoukalas, M. Z., Gavriilaki, E., Gerber, G. F., Konstantakatos, G., Skentou, A. D., Triantafyllidis, L., Kotsiou, N., et al. (2024) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.18105 · Cited by 25

Improving Text Classification Performance with Random Forests-Based Feature Selection

Maruf, S., Javed, K., & Babri, H. A. (2015) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-015-1945-x · Cited by 25

Automatically detecting the scopes of source code comments

Chen, H., Huang, Y., Liu, Z., Chen, X., Zhou, F., & Luo, X. (2019) · Journal of Systems and Software · doi:10.1016/j.jss.2019.03.010 · Cited by 25

Filter Variable Selection Algorithm Using Risk Ratios for Dimensionality Reduction of Healthcare Data for Classification

Bodur, E. K., & Atsa’am, D. D. (2019) · Processes · doi:10.3390/pr7040222 · Cited by 25

Quantifying livestock vulnerability to snow disasters in the Tibetan Plateau: Comparing different modeling techniques for prediction

Ye, T., Liu, W., Mu, Q., Zong, S., Li, Y., & Shi, P. (2020) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2020.101578 · Cited by 25

RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction

Bian, K., Zhou, M., Hu, F., & Lai, W. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.566057 · Cited by 25

Machine learning to predict early TNF inhibitor users in patients with ankylosing spondylitis

Lee, S., Eun, Y., Kim, H., Cha, H., Koh, E., & Lee, J. (2020) · Scientific Reports · doi:10.1038/s41598-020-75352-7 · Cited by 25

Evolutionary model discovery of causal factors behind the socio-agricultural behavior of the Ancestral Pueblo

Gunaratne, C., & Garibay, I. I. (2020) · PLoS ONE · doi:10.1371/journal.pone.0239922 · Cited by 25

Breath biomarkers of insulin resistance in pre-diabetic Hispanic adolescents with obesity

Khan, M. S., Cuda, S. E., Karere, G. M., Cox, L. A., & Bishop, A. C. (2022) · Scientific Reports · doi:10.1038/s41598-021-04072-3 · Cited by 25

Sampling scheme optimization to map soil depth to petrocalcic horizon at field scale

Domenech, M. B., Franco, M. C., Costa, J. L., & Amiotti, N. M. (2016) · Geoderma · doi:10.1016/j.geoderma.2016.12.012 · Cited by 25

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

Joel, S., Eastwick, P. W., & Finkel, E. J. (2017) · doi:10.31219/osf.io/gu8z7 · Cited by 25

Cloud-edge-end-based aircraft assembly production quality monitoring system framework and applications

Zhang, Q., Zhang, Y., Luo, Q., Yu, C., Yu, N., Wang, Q., & Ke, Y. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.06.002 · Cited by 24

An explainable ensemble deep learning model for long-term streamflow forecasting under multiple uncertainties

Qian, X., Wang, B., Chen, J., Fan, Y., Mo, R., Xu, C., Liu, W., Liu, J., & Zhong, P.-A. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.133968 · Cited by 28

Selection of Numerical Weather Forecast Features for PV Power Predictions with Random Forests

Wolff, B., Krämer, O., & Heinemann, D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-50947-1_8 · Cited by 24

Using Growing-Season Time Series Coherence for Improved Peatland Mapping: Comparing the Contributions of Sentinel-1 and RADARSAT-2 Coherence in Full and Partial Time Series

Millard, K., Kirby, P., Nandlall, S. D., Behnamian, A., Banks, S. N., & Pacini, F. (2020) · Remote Sensing · doi:10.3390/rs12152465 · Cited by 24

Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River

Xiong, J., Guo, S., & Yin, J. (2021) · Remote Sensing · doi:10.3390/rs13122272 · Cited by 24

Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem

Rojas, L. A. R., Moletto-Lobos, I. G., Corradini, F., Mattar, C., Fuster, R., & Escobar-Avaria, C. (2021) · Remote Sensing · doi:10.3390/rs13204105 · Cited by 24

Intracranial Aneurysm Rupture Risk Estimation With Multidimensional Feature Fusion

An, X., He, J., Di, Y., Wang, M., Luo, B., Huang, Y., & Ming, D. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.813056 · Cited by 24

Remote Sensing and Meteorological Data Fusion in Predicting Bushfire Severity: A Case Study from Victoria, Australia

Sharma, S. K., Aryal, J., & Rajabifard, A. (2022) · Remote Sensing · doi:10.3390/rs14071645 · Cited by 24

Cost-effective assessment of in-service asphalt pavement condition based on Random Forests and regression analysis

Guo, W., Zhang, J., Cao, D., & Yao, H. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2022.127219 · Cited by 24

Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities

Song, C., Yin, H., Shi, X., Xie, M., Yang, S., Zhou, J., Wang, X., Tang, Z., Yang, Y., & Pan, J. (2022) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2022.103078 · Cited by 24

Natural and human activities driving the spatiotemporal variability of water clarity in lakes across Eastern China

Shen, M., Cao, Z., Xue, K., Liu, D., Qi, T., Ma, J., & Duan, H. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.103037 · Cited by 24

SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors

Irshad, M. T., Nisar, M. A., Huang, X., Hartz, J., Flak, O., Li, F., Gouverneur, P., Piet, A., Oltmanns, K. M., & Grzegorzek, M. (2022) · Sensors · doi:10.3390/s22207711 · Cited by 24

Learning analytics application to examine validity and generalizability of game‐based assessment for spatial reasoning

Kim, Y. J., Knowles, M. A., Scianna, J., Lin, G. C., & Ruipérez‐Valiente, J. A. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13286 · Cited by 24

Variable ranking and selection with random forest for unbalanced data

Bradter, U., Altringham, J. D., Kunin, W. E., Thom, T. J., O’Connell, J., & Benton, T. G. (2022) · Environmental Data Science · doi:10.1017/eds.2022.34 · Cited by 24

The relationship between physical activity and the severity of menopausal symptoms: a cross-sectional study

Wu, S., Shi, Y., Zhao, Q., & Men, K. (2023) · BMC Women s Health · doi:10.1186/s12905-023-02347-7 · Cited by 24

Efficient and accurate TEC modeling and prediction approach with random forest and Bi-LSTM for large-scale region

Jiang, Z., Zhang, Z., He, X., Li, Y., & Yuan, H. (2023) · Advances in Space Research · doi:10.1016/j.asr.2023.09.003 · Cited by 24

Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning

Valizadeh, A., Amirhosseini, M. H., & Ghorbani, Y. (2024) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2024.108623 · Cited by 27

Spatial and temporal variation of algal assemblages in six Midwest agricultural streams having varying levels of atrazine and other physicochemical attributes

Andrus, J. M., Winter, D. M., Scanlan, M. J., Sullivan, S., Bollman, W., Waggoner, J., Hosmer, A. J., & Brain, R. A. (2014) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2014.09.033 · Cited by 24

A novel random forests-based feature selection method for microarray expression data analysis

Yao, D., Yang, J., Zhan, X., Zhan, X., & Xie, Z. (2015) · International Journal of Data Mining and Bioinformatics · doi:10.1504/ijdmb.2015.070852 · Cited by 24

Multivariate random forest models of estuarine-associated fish and invertebrate communities

Miller, K., Huettmann, F., Norcross, B., & Lorenz, M. (2013) · Marine Ecology Progress Series · doi:10.3354/meps10659 · Cited by 24

Predicting Sense of Community and Participation by Applying Machine Learning to Open Government Data

Piscopo, A., Siebes, R., & Hardman, L. (2017) · Policy & Internet · doi:10.1002/poi3.145 · Cited by 24

The facial expression recognition method of random forest based on improved PCA extracting feature

Jia, J., Xu, Y., Zhang, S., & Xue, X. (2016) · IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) · doi:10.1109/icspcc.2016.7753643 · Cited by 24

Estimating Stem Diameter Distributions in a Management Context for a Tolerant Hardwood Forest Using ALS Height and Intensity Data

Shang, C., Treitz, P., Caspersen, J. P., & Jones, T. A. (2016) · Canadian Journal of Remote Sensing · doi:10.1080/07038992.2017.1263152 · Cited by 24

Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa

Kapwata, T., & Gebreslasie, M. T. (2016) · Geospatial health · doi:10.4081/gh.2016.434 · Cited by 24

Multi-modal data fusion for pain intensity assessment and classification

Thiam, P., & Schwenker, F. (2017) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2017.8310115 · Cited by 24

Immunologic findings precede rapid lupus flare after transient steroid therapy

Lu, R., Guthridge, J. M., Chen, H., Bourn, R. L., Kamp, S., Munroe, M. E., Macwana, S., Bean, K. M., Sridharan, S., Merrill, J. T., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-45135-w · Cited by 24

How do physical and social factors affect urban landscape patterns in intermountain basins in Southwest China?

Wang, C., Wu, D., Shen, Z., Peng, M., & Ou, X. (2021) · Landscape Ecology · doi:10.1007/s10980-020-01182-7 · Cited by 24

Predicting weathering indices in soils using FTIR spectra and random forest models

Goydaragh, M. G., Taghizadeh‐Mehrjardi, R., Golchin, A., Jafarzadeh, A. A., & Lado, M. (2021) · CATENA · doi:10.1016/j.catena.2021.105437 · Cited by 24

Is It Possible to Forecast the Price of Bitcoin?

Chevallier, J., Guégan, D., & Goutte, S. (2021) · Forecasting · doi:10.3390/forecast3020024 · Cited by 24

Forecasting the next likely purchase events of insurance customers: A case study on the value of data-rich multichannel environments

Mau, S., Pletikosa, I., & Wagner, J. (2018) · International Journal of Bank Marketing · doi:10.1108/ijbm-11-2016-0180 · Cited by 23

Data-Driven Fault Classification in Large-Scale Industrial Processes Using Reduced Number of Process Variables

Yassaie, N., Gargoum, S., & Al-Dabbagh, A. W. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3317978 · Cited by 23

Machine Learning Modelling for Predicting the Efficacy of Ionic Liquid-Aided Biomass Pretreatment

Mahanty, B., Gharami, M., & Haldar, D. (2024) · BioEnergy Research · doi:10.1007/s12155-024-10747-2 · Cited by 23

Reversing the U: New Evidence on the Internet and Democracy Relationship

Zang, L., Xiong, F., & Gao, Y. (2018) · Social Science Computer Review · doi:10.1177/0894439318767957 · Cited by 23

Predictors determining the potential of inland valleys for rice production development in West Africa

Djagba, J. F., Sintondji, L. O., Kouyaté, A. M., Baggie, I., Agbahungba, G., Hamadoun, A., & Zwart, S. J. (2018) · Applied Geography · doi:10.1016/j.apgeog.2018.05.003 · Cited by 23

Using Machine Learning to Predict 30-Day Hospital Readmissions in Patients with Atrial Fibrillation Undergoing Catheter Ablation

Hung, M., Lauren, E., Hon, E. S., Xu, J., Ruiz‐Negrón, B., Rosales, M. N., Li, W., Barton, T., O’Brien, J., & Su, W. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10030082 · Cited by 23

Improvement of random forest by multiple imputation applied to tower crane accident prediction with missing data

Jiang, L. Y., Zhao, T., Feng, C., & Zhang, W. (2021) · Engineering Construction & Architectural Management · doi:10.1108/ecam-07-2021-0606 · Cited by 23

Integrating the artificial intelligence and hybrid machine learning algorithms for improving the accuracy of spatial prediction of landslide hazards in Kurseong Himalayan Region

Saha, A. K., & Saha, S. (2022) · Artificial Intelligence in Geosciences · doi:10.1016/j.aiig.2022.06.002 · Cited by 23

Feature selection for distance-based regression: An umbrella review and a one-shot wrapper

Linja, J., Hämäläinen, J., Nieminen, P., & Kärkkäinen, T. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.023 · Cited by 23

CRISIS AFAR: an international collaborative study of the impact of the COVID-19 pandemic on mental health and service access in youth with autism and neurodevelopmental conditions

Vibert, B., Segura, P., Gallagher, L., Georgiades, S., Pervanidou, P., Thurm, A. E., Alexander, L. M., Anagnostou, E. A., Aoki, Y. Y., Birken, C. S., et al. (2023) · Molecular Autism · doi:10.1186/s13229-022-00536-z · Cited by 25

Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests

Ma, Y., Zhang, L., Im, J., Zhao, Y., & Zhen, Z. (2023) · Remote Sensing · doi:10.3390/rs15184364 · Cited by 23

Applying precision methods to treatment selection for moderate/severe depression in person-centered experiential therapy or cognitive behavioral therapy

Moggia, D., Saxon, D. H., Lutz, W., Hardy, G. E., & Barkham, M. (2023) · Psychotherapy Research · doi:10.1080/10503307.2023.2269297 · Cited by 22

Exceptional variability in historical fire regimes across a western Cascades landscape, Oregon, USA

Johnston, J. D., Schmidt, M. R., Merschel, A. G., Downing, W. M., Coughlan, M. R., & Lewis, D. G. (2023) · Ecosphere · doi:10.1002/ecs2.4735 · Cited by 23

A comprehensive evaluation of influencing factors of neonicotinoid insecticides (NEOs) in farmland soils across China: First focus on film mulching

Hou, J., Wang, L., Wang, J., Chen, L., Han, B., Li, Y., Yu, L., & Liu, W. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.134284 · Cited by 23

Determining the susceptibility ofEucalyptus nitensforests toCoryphodema tristis(cossid moth) occurrence in Mpumalanga, South Africa

Adam, E., Mutanga, O., & Ismail, R. (2013) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2013.772183 · Cited by 23

Spatial outlier detection in the PM 10 monitoring network of Normandy (France)

Bobbia, M., Misiti, M., Misiti, Y., Poggi, J., & Portier, B. (2015) · Atmospheric Pollution Research · doi:10.5094/apr.2015.053 · Cited by 23

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