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

Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study

Zhang, J., Li, J., Menenti, M., & Hu, G. (2019) · Remote Sensing · doi:10.3390/rs11040452 · Cited by 76

How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset

Tyralis, H., Papacharalampous, G., & Tantanee, S. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.04.070 · Cited by 75

Do airbnb host listing attributes influence room pricing homogenously?

Chattopadhyay, M., & Mitra, S. K. (2019) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2019.03.008 · Cited by 75

Predicting thermal reference conditions for USA streams and rivers

Hill, R. A., Hawkins, C. P., & Carlisle, D. M. (2013) · Freshwater Science · doi:10.1899/12-009.1 · Cited by 75

Efficient machine learning based detection of heart disease

Buettner, R., & Schunter, M. (2019) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom46333.2019.9009429 · Cited by 74

Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods

Chen, Z., Jia, K., Xiao, C., Wei, D., Zhao, X. H., Lan, J., Wei, X., Yao, Y., Wang, B., Sun, Y., et al. (2020) · Remote Sensing · doi:10.3390/rs12132110 · Cited by 74

ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data

Yao, Y., Lv, Y., Tong, L., Liang, Y., Xi, S., Ji, B., Zhang, G., Li, L., Tian, G., Tang, M., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac448 · Cited by 74

Predicting Palustrine Wetland Probability Using Random Forest Machine Learning and Digital Elevation Data-Derived Terrain Variables

Maxwell, A. E., Warner, T. A., & Strager, M. P. (2016) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.82.6.437 · Cited by 75

Windthrow Detection in European Forests with Very High-Resolution Optical Data

Einzmann, K., Immitzer, M., Böck, S., Bauer, O., Schmitt, A., & Atzberger, C. (2017) · Forests · doi:10.3390/f8010021 · Cited by 74

Detecting cocoa plantations in Côte d’Ivoire and Ghana and their implications on protected areas

Abu, I., Szantoi, Z., Brink, A. B., Robuchon, M., & Thiel, M. A. (2021) · Ecological Indicators · doi:10.1016/j.ecolind.2021.107863 · Cited by 79

Flood loss estimation using 3D city models and remote sensing data

Schröter, K., Lüdtke, S., Redweik, R., Meier, J., Bochow, M., Ross, L., Nagel, C., & Kreibich, H. (2018) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2018.03.032 · Cited by 73

An efficient random forests algorithm for high dimensional data classification

Wang, Q., Nguyen, T.-T., Huang, J. Z., & Thủy, N. T. T. (2018) · Advances in Data Analysis and Classification · doi:10.1007/s11634-018-0318-1 · Cited by 72

Specific heat capacity of molten salt-based nanofluids in solar thermal applications: A paradigm of two modern ensemble machine learning methods

Jamei, M., Karbasi, M., Olumegbon, I. A., Mosharaf‐Dehkordi, M., Ahmadianfar, I., & Asadi, A. (2021) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2021.116434 · Cited by 72

Comparison of models describing forest inventory attributes using standard and voxel-based lidar predictors across a range of pulse densities

Pearse, G. D., Watt, M. S., Dash, J. P., Stone, C., & Caccamo, G. (2018) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2018.10.008 · Cited by 72

Spectral features selection and classification of oil palm leaves infected by Basal stem rot (BSR) disease using dielectric spectroscopy

Khaled, A. Y., Aziz, S. A., Bejo, S. K., Nawi, N. M., & Seman, I. A. (2017) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2017.11.012 · Cited by 71

Mapping pasture biomass in Mongolia using Partial Least Squares, Random Forest regression and Landsat 8 imagery

Otgonbayar, M., Atzberger, C., Chambers, J., & Damdinsuren, A. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1541110 · Cited by 71

Machine learning approaches for estimation of compressive strength of concrete

Hadzima-Nyarko, M., Nyarko, E. K., Lü, H., & Zhu, S. (2020) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-020-00703-2 · Cited by 71

Machine Learning-Based Presymptomatic Detection of Rice Sheath Blight Using Spectral Profiles

Conrad, A. O., Li, W., Lee, D.-Y., Wang, G., RODRIGUEZ‐SAONA, L. E., & Bonello, P. (2020) · Plant Phenomics · doi:10.34133/2020/8954085 · Cited by 71

Optimization modelling to establish false measures implemented with ex-situ plant species to control gully erosion in a monsoon-dominated region with novel in-situ measurements

Saha, A., Pal, S. C., Arabameri, A., Chowdhuri, I., Rezaie, F., Chakrabortty, R., Roy, P., & Shit, M. (2021) · Journal of Environmental Management · doi:10.1016/j.jenvman.2021.112284 · Cited by 71

Combining random forests and class-balancing to discriminate between three classes of avalanche activity in the French Alps

Sielenou, P. D., Viallon-Galinier, L., Hagenmuller, P., Naveau, P., Morin, S., Dumont, M., Verfaillie, D., & Eckert, N. (2021) · Cold Regions Science and Technology · doi:10.1016/j.coldregions.2021.103276 · Cited by 71

Human or natural? Landscape context improves the attribution of forest disturbances mapped from Landsat in Central Europe

Sebald, J., Senf, C., & Seidl, R. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112502 · Cited by 74

Data‐Driven Forecasting of Low‐Latitude Ionospheric Total Electron Content Using the Random Forest and LSTM Machine Learning Methods

Zewdie, G. K., Valladares, C. E., Cohen, M. B., Lary, D. J., Ramani, D., & Tsidu, G. M. (2021) · Space Weather · doi:10.1029/2020sw002639 · Cited by 75

Flash Flood Water Depth Estimation Using SAR Images, Digital Elevation Models, and Machine Learning Algorithms

Elkhrachy, I. (2022) · Remote Sensing · doi:10.3390/rs14030440 · Cited by 71

Prediction of Probability of Liquefaction Using Soft Computing Techniques

Kumar, D. R., Samui, P., & Burman, A. (2022) · Journal of The Institution of Engineers (India) Series A · doi:10.1007/s40030-022-00683-9 · Cited by 72

Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models

Luo, M., Anees, S. A., Huang, Q., Xin, Q., Qin, Z., Fan, J., Han, G., Zhang, L., & Shafri, H. Z. M. (2024) · Forests · doi:10.3390/f15060975 · Cited by 71

A Landslide Susceptibility Assessment Method Based on GIS Technology and an AHP-Weighted Information Content Method: A Case Study of Southern Anhui, China

He, H., Hu, D., Sun, Q., Zhu, L., & Liu, Y. (2019) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi8060266 · Cited by 71

Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

Aziz, F., Malek, S., Ibrahim, K. S., Shariff, R. E. R., Ahmad, W. A. W., Ali, R. M., Liu, K. T., Selvaraj, G., & Kasim, S. S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254894 · Cited by 71

Predictive Analysis of Students’ Learning Performance Using Data Mining Techniques: A Comparative Study of Feature Selection Methods

Mustapha, S. M. F. D. S. (2023) · Applied System Innovation · doi:10.3390/asi6050086 · Cited by 70

Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China

Li, Z., Xin, X., Tang, H., Yang, F., Chen, B., & ZHANG, B.-H. (2017) · Journal of Integrative Agriculture · doi:10.1016/s2095-3119(15)61303-x · Cited by 70

An ensemble decision tree methodology for remaining useful life prediction of spur gears under natural pitting progression

Kundu, P., Darpe, A. K., & Kulkarni, M. S. (2019) · Structural Health Monitoring · doi:10.1177/1475921719865718 · Cited by 70

Contributions of reader- and text-level characteristics to eye-movement patterns during passage reading.

Kuperman, V., Matsuki, K., & Dyke, J. A. V. (2018) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/xlm0000547 · Cited by 69

Measuring urban landscapes for urban function classification using spatial metrics

Xing, H., & Meng, Y. (2019) · Ecological Indicators · doi:10.1016/j.ecolind.2019.105722 · Cited by 71

A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia

Mei, Y., Maggioni, V., Houser, P. R., Xue, Y., & Rouf, T. (2020) · Water Resources Research · doi:10.1029/2020wr027472 · Cited by 69

Comparison of linear regression, k-nearest neighbour and random forest methods in airborne laser-scanning-based prediction of growing stock

Cosenza, D. N., Korhonen, L., Maltamo, M., Packalén, P., Strunk, J. L., Næsset, E., Gobakken, T., Soares, P., & Tomé, M. (2020) · Forestry An International Journal of Forest Research · doi:10.1093/forestry/cpaa034 · Cited by 69

Hierarchical diversity entropy for the early fault diagnosis of rolling bearing

Wang, X., Si, S., & Li, Y. (2022) · Nonlinear Dynamics · doi:10.1007/s11071-021-06728-1 · Cited by 69

Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada

Laamrani, A., van den Berg, A. A., Voroney, P. R., Feilhauer, H., Blackburn, L., March, M., Dao, P. D., He, Y., & Martin, R. C. (2019) · Remote Sensing · doi:10.3390/rs11111298 · Cited by 68

CART Decision Tree Combined with Boruta Feature Selection for Medical Data Classification

Tang, R., & Zhang, X. (2020) · IEEE International Conference on Big Data Analytics (ICBDA) · doi:10.1109/icbda49040.2020.9101199 · Cited by 68

Urban Heat Islands Significantly Reduced by COVID‐19 Lockdown

Liu, Z., Lai, J., Zhan, W., Bechtel, B., Voogt, J., Quan, J., Hu, L., Fu, P., Huang, F., Li, L., et al. (2022) · Geophysical Research Letters · doi:10.1029/2021gl096842 · Cited by 68

Feature selection for high-dimensional multi-category data using PLS-based local recursive feature elimination

You, W., Yang, Z., & Ji, G. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.08.043 · Cited by 68

A statistical comparison of neuroclassifiers and feature selection methods for gearbox fault diagnosis under realistic conditions

Pacheco, F., de Oliveira, J. V., Sánchez, R., Cerrada, M., Cabrera, D. R., Li, C., Zurita, G., & Artés, M. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.02.028 · Cited by 68

Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance

Sun, J. Y., Yang, J., Shi, S., Chen, B., Du, L., Gong, W. Y., & Song, S. (2017) · Remote Sensing · doi:10.3390/rs9090951 · Cited by 69

MSIpred: a python package for tumor microsatellite instability classification from tumor mutation annotation data using a support vector machine

Wang, C., & Liang, C. (2018) · Scientific Reports · doi:10.1038/s41598-018-35682-z · Cited by 68

An improved deep forest model for forecast the outdoor atmospheric corrosion rate of low-alloy steels

Zhi, Y., Yang, T., & Fu, D. (2020) · Journal of Material Science and Technology · doi:10.1016/j.jmst.2020.01.044 · Cited by 67

Farm efficiency estimation using a hybrid approach of machine-learning and data envelopment analysis: Evidence from rural eastern India

Nandy, A., & Singh, P. K. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.122106 · Cited by 68

A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network

Wang, N., Peng, J. G., Xue, J., Zhang, X., Huang, J., Biswas, A., He, Y., & Shi, Z. (2021) · Geoderma · doi:10.1016/j.geoderma.2021.115656 · Cited by 67

Impact of time to intubation on mortality and pulmonary sequelae in critically ill patients with COVID-19: a prospective cohort study

González, J., Benitez, I. D., de Gonzalo‐Calvo, D., Torres, G., de Batlle, J., Gómez, S., Moncusí‐Moix, A., Carmona, P., Santisteve, S., Monge, A., et al. (2022) · Critical Care · doi:10.1186/s13054-021-03882-1 · Cited by 67

Feature selection and importance of predictors of non-communicable diseases medication adherence from machine learning research perspectives

Kanyongo, W., & Ezugwu, A. E.-S. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101232 · Cited by 67

Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images

Zhang, L., Zhaoning, G., Qiwei, W., Jin, D., & Wang, X. (2019) · National Remote Sensing Bulletin · doi:10.11834/jrs.20198083 · Cited by 67

Risk assessment of bridge construction investigated using random forest algorithm

Wu, Y., Wang, Y., Liu, H., Xie, L., Jiao, L., & Lu, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-72051-5 · Cited by 67

Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images

Feeny, A. K., Tadarati, M., Freund, D., Bressler, N. M., & Burlina, P. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.06.018 · Cited by 67

Modeling of free swelling index based on variable importance measurements of parent coal properties by random forest method

Chelgani, S. C., Matin, S. S., & Makaremi, S. (2016) · Measurement · doi:10.1016/j.measurement.2016.07.070 · Cited by 67

Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection

Kuehlkamp, A., Pinto, A. D. S., Rocha, A., Bowyer, K. W., & Czajka, A. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2878542 · Cited by 66

Diversity of functional trade‐offs enhances survival after fire in Neotropical savanna species

Scalon, M. C., Domingos, F. M. C. B., da Cruz, W. J. A., Marimon-Junior, B. H., Marimon, B. S., & Menor, I. O. (2019) · Journal of Vegetation Science · doi:10.1111/jvs.12823 · Cited by 66

Neuroanatomical correlates of cognitive functioning in prodromal Huntington disease

Harrington, D. L., Liu, D., Smith, M. M., Mills, J. A., Long, J. D., Aylward, E., Paulsen, J. S., & PREDICT‐HD Investigators Coordinators of the Huntington Study Group (2013) · Brain and Behavior · doi:10.1002/brb3.185 · Cited by 66

Comparison of conditioned Latin hypercube and feature space coverage sampling for predicting soil classes using simulation from soil maps

Ma, T., Brus, D. J., Zhu, A., Zhang, L., & Scholten, T. (2020) · Geoderma · doi:10.1016/j.geoderma.2020.114366 · Cited by 66

Regression-Enhanced Random ForestsPreprint

Zhang, H.-Z., Nettleton, D., & Zhu, Z.-Y. (2019) · arXiv.org · doi:10.48550/arxiv.1904.10416 · Cited by 54

Spatial and temporal trends of soil total nitrogen and C/N ratio for croplands of East China

Deng, X., Ma, W., Ren, Z., Zhang, M., Grieneisen, M. L., Chen, X., Fei, X., Qin, F., Zhan, Y., & Lv, X. (2019) · Geoderma · doi:10.1016/j.geoderma.2019.114035 · Cited by 65

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

Zaman, N. A. F. K., Kanniah, K. D., Kaskaoutis, D. G., & Latif, M. T. (2021) · Applied Sciences · doi:10.3390/app11167326 · Cited by 65

Integration of shapley additive explanations with random forest model for quantitative precipitation estimation of mesoscale convective systems

He, Z., Yang, Y., Fang, R., Zhou, S., Zhao, W., Bai, Y., Li, J., & Wang, B. (2023) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.1057081 · Cited by 67

Integration of Sentinel-1/2 and topographic attributes to predict the spatial distribution of soil texture fractions in some agricultural soils of western Iran

Azizi, K., Garosi, Y., Ayoubi, S., & Tajik, S. (2023) · Soil and Tillage Research · doi:10.1016/j.still.2023.105681 · Cited by 66

ESG Factors and Firms' Credit Risk

Bonacorsi, L., Cerasi, V., Galfrascoli, P., & Manera, M. (2024) · Journal of Climate Finance · doi:10.1016/j.jclimf.2024.100032 · Cited by 65

Satellites Reveal Spatial Heterogeneity in Dryland Photovoltaic Plants' Effects on Vegetation Dynamics

Xia, Z., Li, Y., Guo, S., Chen, R., Zhang, W., Guo, X., Zhang, X., & Du, P. (2024) · Earth s Future · doi:10.1029/2024ef004427 · Cited by 65

Feature selection and classification of leukocytes using random forest

Saraswat, M., & Arya, K. V. (2014) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-014-1200-8 · Cited by 65

Importance analysis and meta-model construction with correlated variables in evaluation of thermal performance of campus buildings

Tian, W., Choudhary, R., Augenbroe, G., & Lee, S. H. (2015) · Building and Environment · doi:10.1016/j.buildenv.2015.04.021 · Cited by 65

Using Machine Learning to Detect ‘Multiple-Account’ Cheating and Analyze the Influence of Student and Problem Features

Ruipérez‐Valiente, J. A., Muñoz‐Merino, P. J., Alexandron, G., & Pritchard, D. E. (2017) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2017.2784420 · Cited by 65

An improved algorithm for identifying shallow and deep-seated landslides in dense tropical forest from airborne laser scanning data

Mezaal, M. R., & Pradhan, B. (2018) · CATENA · doi:10.1016/j.catena.2018.04.038 · Cited by 65

Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis

Creagh, A. P., Hamy, V., Yuan, H., Mertes, G., Tomlinson, R., Chen, W.-H., Williams, R., Llop, C., Yee, C., Duh, M. S., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01013-y · Cited by 64

Quantifying the influencing factors and multi-factor interactions affecting cadmium accumulation in limestone-derived agricultural soil using random forest (RF) approach

Li, X., Geng, T., Shen, W., Zhang, J., & Zhou, Y. (2020) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2020.111773 · Cited by 64

Prediction of Mechanical Properties of Wrought Aluminium Alloys Using Feature Engineering Assisted Machine Learning Approach

Hu, M., Tan, Q., Knibbe, R., Wang, S., Li, X., Wu, T., Jarin, S., & Zhang, M. (2021) · Metallurgical and Materials Transactions A · doi:10.1007/s11661-021-06279-5 · Cited by 64

Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse

Gowin, J. L., Ball, T. M., Wittmann, M., Tapert, S. F., & Paulus, M. P. (2015) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2015.04.018 · Cited by 64

Random forest-based approach for physiological functional variable selection for driver’s stress level classification

Elhaouij, N., Poggi, J., Ghozi, R., Sevestre-Ghalila, S., & Jaïdane-Saïdane, M. (2018) · Statistical Methods & Applications · doi:10.1007/s10260-018-0423-5 · Cited by 70

Predicting Delivery Capability in Iterative Software Development

Choetkiertikul, M., Dam, H. K., Tran, T., Ghose, A., & Grundy, J. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2017.2693989 · Cited by 64

Investigating the organic and conventional production type of olive oil with target and suspect screening by LC-QTOF-MS, a novel semi-quantification method using chemical similarity and advanced chemometrics

Kalogiouri, N. P., Aalizadeh, R., & Τhomaidis, Ν. S. (2017) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-017-0395-6 · Cited by 64

Improvement of rainfall estimation from MSG data using Random Forests classification and regression

Ouallouche, F., Lazri, M., & Ameur, S. (2018) · Atmospheric Research · doi:10.1016/j.atmosres.2018.05.001 · Cited by 64

Characterization and Mapping of a Deep-Sea Sponge Ground on the Tropic Seamount (Northeast Tropical Atlantic): Implications for Spatial Management in the High Seas

Ramiro‐Sánchez, B., González‐Irusta, J. M., Henry, L., Cleland, J., Yeo, I., Xavier, J. R., Carreiro‐Silva, M., Sampaio, Í., Spearman, J. R., Victorero, L., et al. (2019) · Frontiers in Marine Science · doi:10.3389/fmars.2019.00278 · Cited by 64

Diverse usage of waterbird habitats and spatial management in Yancheng coastal wetlands

Wang, C., Wang, G., Dai, L., Liu, H., Li, Y., Zhou, Y., Chen, H., Dong, B., Lv, S., & Zhao, Y. (2020) · Ecological Indicators · doi:10.1016/j.ecolind.2020.106583 · Cited by 63

Mapping homogeneous regions for flash floods using machine learning: A case study in Jiangxi province, China

Zhang, R., Chen, Y., Zhang, X., Ma, Q., & Ren, L. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102717 · Cited by 63

Human footprint is associated with shifts in the assemblages of major vector-borne diseases

Skinner, E. B., Glidden, C. K., MacDonald, A. J., & Mordecai, E. A. (2023) · Nature Sustainability · doi:10.1038/s41893-023-01080-1 · Cited by 63

Water table depth forecasting in cranberry fields using two decision-tree-modeling approaches

Brédy, J., Gallichand, J., Célicourt, P., & Gumière, S. J. (2020) · Agricultural Water Management · doi:10.1016/j.agwat.2020.106090 · Cited by 63

Classification of motor errors to provide real-time feedback for sports coaching in virtual reality — A case study in squats and Tai Chi pushes

Hülsmann, F., Göpfert, J. P., Hammer, B., Kopp, S., & Botsch, M. (2018) · Computers & Graphics · doi:10.1016/j.cag.2018.08.003 · Cited by 66

Generalised Additive Models and Random Forest Approach as effective methods for predictive species density and functional species richness

Kosicki, J. Z. (2020) · Environmental and Ecological Statistics · doi:10.1007/s10651-020-00445-5 · Cited by 62

Coherent Streamflow Variability in Monsoon Asia Over the Past Eight Centuries—Links to Oceanic Drivers

Nguyen, H. T. T., Turner, S. W. D., Buckley, B. M., & Galelli, S. (2020) · Water Resources Research · doi:10.1029/2020wr027883 · Cited by 62

Circulating microRNA-based screening tool for breast cancer

Frères, P., Wenric, S., Boukerroucha, M., Fasquelle, C., Thiry, J., Bovy, N., Struman, I., Geurts, P., Collignon, J., Schroeder, H., et al. (2015) · Oncotarget · doi:10.18632/oncotarget.6786 · Cited by 62

Modeling the response of negative air ions to environmental factors using multiple linear regression and random forest

Shi, G.-Y., Zhou, Y., Sang, Y.-Q., Huang, H., Zhang, J., Meng, P., & Cai, L.-L. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101464 · Cited by 61

Practical guide for retaining correlated climate variables and unthinned samples in species distribution modeling, using random forests

Hanberry, B. B. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102406 · Cited by 61

Prediction of froth flotation responses based on various conditioning parameters by Random Forest method

Shahbazi, B., Chelgani, S. C., & Matin, S. S. (2017) · Colloids and Surfaces A Physicochemical and Engineering Aspects · doi:10.1016/j.colsurfa.2017.07.013 · Cited by 61

Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes

Verhoeven, T., Coito, A. L., Plomp, G., Thomschewski, A. C., Pittau, F., Trinka, E., Wiest, R., Schaller, K., Michel, C. M., Seeck, M., et al. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.09.021 · Cited by 61

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Jing, Y., Hu, Z., Fan, P., Xue, Y., Wang, L., Tarter, R. E., Kirisci, L., Wang, J., Vanyukov, M. M., & Xie, X. (2019) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2019.107605 · Cited by 67

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Sethi, S. A., Reimer, M., & Knapp, G. (2014) · Marine Policy · doi:10.1016/j.marpol.2014.03.027 · Cited by 60

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Liu, M., Liu, X., Liu, D., Ding, C., & Jiang, J. (2014) · Computers & Geosciences · doi:10.1016/j.cageo.2014.10.016 · Cited by 59

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Merentitis, A., Debes, C., & Heremans, R. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2013.2295513 · Cited by 61

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Vetter, S. G., Brandstätter, C. A., Macheiner, M., Suchentrunk, F., Gerritsmann, H., & Bieber, C. (2016) · Animal Behaviour · doi:10.1016/j.anbehav.2016.03.026 · Cited by 59

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

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

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

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