Classification and Regression Trees.

A Survey of Methods for Scaling Up Inductive Algorithms

Provost, F., & Kolluri, V. (1999) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009876119989 · Cited by 277

Randomizing Outputs to Increase Prediction Accuracy

Breiman, L. (2000) · Machine Learning · doi:10.1023/a:1007682208299 · Cited by 276

Speeding Up Logistic Model Tree Induction

Sumner, M., Frank, E., & Hall, M. (2005) · Lecture Notes in Computer Science · doi:10.1007/11564126_72 · Cited by 274

Affordance detection of tool parts from geometric features

Myers, A., Teo, C. L., Fermuller, C., & Aloimonos, Y. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139369 · Cited by 273

Decision tree and PCA-based fault diagnosis of rotating machinery

Sun, W., Chen, J., & Li, J. (2007) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2006.06.010 · Cited by 272

Sample Selection Bias Correction Theory

Cortes, C., Mohri, M., Riley, M., & Rostamizadeh, A. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-87987-9_8 · Cited by 272

A novel hybrid model for hourly global solar radiation prediction using random forests technique and firefly algorithm

Ibrahim, I. A., & Khatib, T. (2017) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.02.006 · Cited by 267

New models and online calculator for predicting non-sentinel lymph node status in sentinel lymph node positive breast cancer patients

Kohrt, H. E., Olshen, R. A., Bermas, H. R., Goodson, W. H., Wood, D. J., Henry, S., Rouse, R. V., Bailey, L., Philben, V. J., Dirbas, F. M., et al. (2008) · BMC Cancer · doi:10.1186/1471-2407-8-66 · Cited by 262

Placebo Response in Randomized Controlled Trials of Antidepressants for Pediatric Major Depressive Disorder

Bridge, J. A., Birmaher, B., Iyengar, S., Barbe, R. P., & Brent, D. A. (2009) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2008.08020247 · Cited by 256

The MCCB impairment profile for schizophrenia outpatients: results from the MATRICS psychometric and standardization study

Kern, R. S., Gold, J. M., Dickinson, D., Green, M. F., Nuechterlein, K. H., Baade, L. E., Keefe, R. S. E., Mesholam-Gately, R. I., Seidman, L. J., Lee, C., et al. (2011) · Schizophrenia Research · doi:10.1016/j.schres.2010.11.008 · Cited by 251

Activation of Src kinase in primary colorectal carcinoma: An indicator of poor clinical prognosis

Allgayer, H., Boyd, D. D., Heiss, M. M., Abdalla, E. K., Curley, S. A., & Gallick, G. E. (2002) · Cancer · doi:10.1002/cncr.10221 · Cited by 251

A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds

de Jong, M., Chen, W., Notestine, R., Persson, K., Ceder, G., Jain, A., Asta, M., & Gamst, A. (2016) · Scientific Reports · doi:10.1038/srep34256 · Cited by 243

Decision tree classifiers for automated medical diagnosis

Azar, A. T., & El-Metwally, S. M. (2013) · Neural Computing and Applications · doi:10.1007/s00521-012-1196-7 · Cited by 242

Crocodiles: biology, husbandry and diseases

(2003) · doi:10.1079/9780851996561.0000 · Cited by 242

Prediction of outcome in critically ill patients using artificial neural network synthesised by genetic algorithm.

Dybowski, R., Gant, V, Weller, P., & Chang, R. (1996) · The Lancet · doi:10.1016/s0140-6736(96)90609-1 · Cited by 240

Hydrothermal Carbonization of Organic Waste and Biomass: A Review on Process, Reactor, and Plant Modeling

Ischia, G., & Fiori, L. (2021) · Waste and Biomass Valorization · doi:10.1007/s12649-020-01255-3 · Cited by 234

Cystic Lesions of the Pancreas: Changes in the Presentation and Management of 1,424 Patients at a Single Institution over a 15-Year Time Period

Gaujoux, S., Brennan, M. F., Gonen, M., D’Angelica, M. I., DeMatteo, R., Fong, Y., Schattner, M., DiMaio, C., Janakos, M., Jarnagin, W. R., et al. (2011) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2011.01.016 · Cited by 224

Star–galaxy classification using deep convolutional neural networks

Kim, E. J., & Brunner, R. J. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw2672 · Cited by 221

Temporal and spatial dynamics of diel-cycling hypoxia in estuarine tributaries

Tyler, R. M., Brady, D. C., & Targett, T. E. (2009) · Estuaries and Coasts · doi:10.1007/s12237-008-9108-x · Cited by 217

eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates

Pu, L., Naderi, M., Liu, T., Wu, H.-C., Mukhopadhyay, S., & Brylinski, M. (2019) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-018-0282-6 · Cited by 212

Corporate bankruptcy prediction: a high dimensional analysis

Jones, S. (2017) · Review of Accounting Studies · doi:10.1007/s11142-017-9407-1 · Cited by 214

Short-Term Load Forecasting Using Random Forests

Dudek, G. (2015) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-319-11310-4_71 · Cited by 214

Random forest regression for magnetic resonance image synthesis

Jog, A., Carass, A., Roy, S., Pham, D. L., & Prince, J. L. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.08.009 · Cited by 212

Altered gut flora are associated with septic complications and death in critically ill patients with systemic inflammatory response syndrome

Shimizu, K., Ogura, H., Hamasaki, T., Goto, M., Tasaki, O., Asahara, T., Nomoto, K., Morotomi, M., Matsushima, A., Kuwagata, Y., et al. (2011) · Digestive Diseases and Sciences · doi:10.1007/s10620-010-1418-8 · Cited by 213

Consistent cross-validatory model-selection for dependent data: hv-block cross-validation

Racine, J. (2000) · Journal of Econometrics · doi:10.1016/s0304-4076(00)00030-0 · Cited by 205

Neuronal Prediction of Opponent’s Behavior during Cooperative Social Interchange in Primates

Haroush, K., & Williams, Z. M. (2015) · Cell · doi:10.1016/j.cell.2015.01.045 · Cited by 210

An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor

Sharif, M., Amin, J., Raza, M., Yasmin, M., & Satapathy, S. C. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.017 · Cited by 206

Automatic detection of solar photovoltaic arrays in high resolution aerial imagery

Malof, J. M., Bradbury, K., Collins, L. M., & Newell, R. G. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.08.191 · Cited by 196

Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms

Erdal, H. I., & Karakurt, O. (2013) · Journal of Hydrology · doi:10.1016/j.jhydrol.2012.11.015 · Cited by 191

Improving propensity score estimators’ robustness to model misspecification using super learner.

Pirracchio, R., Petersen, M. L., & van der Laan, M. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwu253 · Cited by 186

Muscle-enhancing behaviors among adolescent girls and boys

Eisenberg, M. E., Wall, M., & Neumark-Sztainer, D. (2012) · Pediatrics · doi:10.1542/peds.2012-0095 · Cited by 197

On Issues of Instance Selection

Liu, H., & Motoda, H. (2002) · Data Mining and Knowledge Discovery · doi:10.1023/a:1014056429969 · Cited by 191

Mortality Risk Score Prediction in an Elderly Population Using Machine Learning

Rose, S. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kws241 · Cited by 193

Depression is the main determinant of quality of life in multiple sclerosis: a classification-regression (CART) study

D'Alisa, S., Miscio, G., Baudo, S., Simone, A., Tesio, L., & Mauro, A. (2006) · Disability and Rehabilitation · doi:10.1080/09638280500191753 · Cited by 182

Decision trees for uplift modeling with single and multiple treatments

Rzepakowski, P., & Jaroszewicz, S. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-011-0434-0 · Cited by 181

Residual clinical damage after COVID-19: a retrospective and prospective observational cohort study

De Lorenzo, R., Conte, C., Lanzani, C., Benedetti, F., Roveri, L., Mazza, M. G., Brioni, E., Giacalone, G., Canti, V., Sofia, V., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0239570 · Cited by 179

Forecasting Murder Within a Population of Probationers and Parolees: A High Stakes Application of Statistical Learning

Berk, R., Sherman, L., Barnes, G., Kurtz, E., & Ahlman, L. (2009) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2008.00556.x · Cited by 178

Predicting bitcoin returns using high-dimensional technical indicators

Huang, J.-Z., Huang, W., & Ni, J. (2019) · The Journal of Finance and Data Science · doi:10.1016/j.jfds.2018.10.001 · Cited by 177

Methods for testing theory and evaluating impact in randomized field trials: Intent-to-treat analyses for integrating the perspectives of person, place, and time

Brown, C. H., Wang, W., Kellam, S. G., Muthén, B. O., Petras, H., Toyinbo, P., Poduska, J., Ialongo, N., Wyman, P. A., Chamberlain, P., et al. (2008) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2007.11.013 · Cited by 177

Assessing the Effect of Race Bias in Post-traffic Stop Outcomes Using Propensity Scores

Ridgeway, G. (2006) · Journal of Quantitative Criminology · doi:10.1007/s10940-005-9000-9 · Cited by 176

Detecting moderator effects using subgroup analyses

Wang, R., & Ware, J. H. (2013) · Prevention Science · doi:10.1007/s11121-011-0221-x · Cited by 165

Application of wrapper approach and composite classifier to the stock trend prediction

HUANG, C., YANG, D., & CHUANG, Y. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.05.035 · Cited by 167

Robust gene expression signature from formalin-fixed paraffin-embedded samples predicts prognosis of non-small-cell lung cancer patients.

Xie, Y., Xiao, G., Coombes, K. R., Behrens, C., Solis, L. M., Raso, G., Girard, L., Erickson, H. S., Roth, J., Heymach, J. V., et al. (2011) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-11-0196 · Cited by 162

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(2015) · International Conference on Artificial Intelligence and Statistics · Cited by 162

Mental Contamination and the Debiasing Problem

Wilson, T. D., Centerbar, D. B., & Brekke, N. (2002) · Heuristics and Biases · doi:10.1017/cbo9780511808098.012 · Cited by 160

Analysis of physiological signals for recognition of boredom, pain, and surprise emotions

Jang, E.-H., Park, B.-J., Park, M.-S., Kim, S.-H., & Sohn, J.-H. (2015) · Journal of Physiological Anthropology · doi:10.1186/s40101-015-0063-5 · Cited by 159

Increased Chest Tube Drainage Is Independently Associated With Adverse Outcome After Cardiac Surgery

Christensen, M. C., Dziewior, F., Kempel, A., & von Heymann, C. (2012) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2011.09.021 · Cited by 158

Traffic flow forecasting for urban work zones

Hou, Y., Edara, P., & Sun, C. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2371993 · Cited by 154

Development and validation of a tool to identify patients with type 2 diabetes at high risk of hypoglycemia-related emergency department or hospital use

Karter, A. J., Warton, E. M., Lipska, K. J., Ralston, J. D., Moffet, H. H., Jackson, G. G., Huang, E. S., & Miller, D. R. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2017.3844 · Cited by 150

The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence

Watson, D. (2019) · Minds and Machines · doi:10.1007/s11023-019-09506-6 · Cited by 148

Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set

Sabhnani, M., & Serpen, G. (2004) · Intelligent Data Analysis · doi:10.3233/ida-2004-8406 · Cited by 148

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Lo (2002) · doi:10.1145/772862.772872 · Cited by 148

The calibration of expert judgment: heuristics and biases beyond the laboratory

Koehler, D. J., Brenner, L., & Griffin, D. (2002) · Heuristics and Biases · doi:10.1017/cbo9780511808098.041 · Cited by 146

Predictors of remission in depression to individual and combined treatments (PReDICT): study protocol for a randomized controlled trial

Dunlop, B. W., Binder, E. B., Cubells, J. F., Goodman, M. M., Kelley, M. E., Kinkead, B., Kutner, M., Nemeroff, C. B., Newport, D. J., Owens, M. J., et al. (2012) · Trials · doi:10.1186/1745-6215-13-106 · Cited by 136

Ranking Malaria Risk Factors to Guide Malaria Control Efforts in African Highlands

Protopopoff, N., Van Bortel, W., Speybroeck, N., Van Geertruyden, J.-P., Baza, D., D'Alessandro, U., & Coosemans, M. (2009) · PLoS ONE · doi:10.1371/journal.pone.0008022 · Cited by 142

Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems

Pliakos, K., Joo, S.-H., Park, J. Y., Cornillie, F., Vens, C., & Van den Noortgate, W. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.04.009 · Cited by 149

Combinations of techniques that effectively change health behavior: evidence from Meta-CART analysis

Dusseldorp, E., van Genugten, L., van Buuren, S., Verheijden, M. W., & van Empelen, P. (2014) · Health Psychology · doi:10.1037/hea0000018 · Cited by 138

Sequence Analysis Using Logic Regression

Kooperberg, C., Ruczinski, I., LeBlanc, M. L., & Hsu, L. (2001) · Genetic Epidemiology · doi:10.1002/gepi.2001.21.s1.s626 · Cited by 138

On switching response surface models, with applications to the structural health monitoring of bridges

Worden, K., & Cross, E. J. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.04.022 · Cited by 135

Stand-replacing patches within a mixed severity fire regime: Quantitative characterization using recent fires in a long-established natural fire area

Collins, B. M., & Stephens, S. L. (2010) · Landscape Ecology · doi:10.1007/s10980-010-9470-5 · Cited by 133

Protected‐Area Boundaries as Filters of Plant Invasions

FOXCROFT, L. C., JAROŠÍK, V., PYŠEK, P., RICHARDSON, D. M., & ROUGET, M. (2011) · Conservation Biology · doi:10.1111/j.1523-1739.2010.01617.x · Cited by 133

Machine Learning in Pharmacometrics: Opportunities and Challenges

McComb, M., Bies, R., & Ramanathan, M. (2022) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14801 · Cited by 128

Predicting the Likelihood of Remission in Children With Graves’ Disease: A Prospective, Multicenter Study

Glaser, N. S., Styne, D. M., & for the Organization of Pediatric Endocrinologists of Northern California Collaborative Graves’ Disease Study Group (2008) · Pediatrics · doi:10.1542/peds.2007-1535 · Cited by 125

Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country

Cruz-Jesus, F., Castelli, M., Oliveira, T., Mendes, R., Nunes, C., Sa-Velho, M., & Rosa-Louro, A. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e04081 · Cited by 118

Predicting Developmental Status from 12 to 24 Months in Infants at Risk for Autism Spectrum Disorder: A Preliminary Report

Macari, S. L., Campbell, D., Gengoux, G. W., Saulnier, C. A., Klin, A. J., & Chawarska, K. (2012) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-012-1521-0 · Cited by 116

Dynamic Trees for Learning and Design

Taddy, M. A., Gramacy, R. B., & Polson, N. G. (2011) · Journal of the American Statistical Association · doi:10.1198/jasa.2011.ap09769 · Cited by 113

Utilizing Electronic Health Records to Predict Acute Kidney Injury Risk and Outcomes: Workgroup Statements from the 15 th ADQI Consensus Conference

Sutherland, S. M., Chawla, L. S., Kane-Gill, S. L., Hsu, R. K., Kramer, A. A., Goldstein, S. L., Kellum, J. A., Ronco, C., Bagshaw, S. M., & on behalf of the 15 ADQI Consensus Group (2016) · Canadian Journal of Kidney Health and Disease · doi:10.1186/s40697-016-0099-4 · Cited by 115

APOE*E2 allele delays age of onset in PSEN1 E280A Alzheimer’s disease

Vélez, J. I., Lopera, F., Sepulveda-Falla, D., Patel, H. R., Johar, A. S., Chuah, A., Tobón, C., Rivera, D., Villegas, A., Cai, Y., et al. (2016) · Molecular Psychiatry · doi:10.1038/mp.2015.177 · Cited by 111

Identity domains capture individual differences from across the behavioral repertoire

Forkosh, O., Karamihalev, S., Roeh, S., Alon, U., Anpilov, S., Touma, C., Nussbaumer, M., Flachskamm, C., Kaplick, P. M., Shemesh, Y., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0516-y · Cited by 110

1. Linking Life Histories and Mental Health: A Person-Centered Strategy

Singer, B., Ryff, C. D., Carr, D., & Magee, W. J. (1998) · Sociological Methodology · doi:10.1111/0081-1750.00041 · Cited by 112

Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

Kochmar, E., Vu, D. D., Belfer, R., Gupta, V., Serban, I. V., & Pineau, J. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00267-x · Cited by 112

Exploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learning

Coşgun, A., Günay, M. E., & Yıldırım, R. (2021) · Renewable Energy · doi:10.1016/j.renene.2020.09.034 · Cited by 110

Learning Certifiably Optimal Rule Lists for Categorical Data

Angelino, E., Larus-Stone, N., Alabi, D., Seltzer, M., & Rudin, C. (2017) · arXiv · doi:10.48550/arxiv.1704.01701 · Cited by 108

Gray matter T2 hypointensity is related to plaques and atrophy in the brains of multiple sclerosis patients

Bakshi, R., Dmochowski, J., Shaikh, Z. A., & Jacobs, L. (2001) · Journal of the Neurological Sciences · doi:10.1016/s0022-510x(01)00477-4 · Cited by 107

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(2017) · doi:10.1145/3097983.3098047 · Cited by 104

The Computerized Adaptive Diagnostic Test for Major Depressive Disorder (CAD-MDD): A Screening Tool for Depression

Gibbons, R. D., Hooker, G., Finkelman, M. D., Weiss, D. J., Pilkonis, P. A., Frank, E., Moore, T., & Kupfer, D. J. (2013) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.12m08338 · Cited by 101

A peek into the black box: exploring classifiers by randomization

Henelius, A., Puolamäki, K., Boström, H., Asker, L., & Papapetrou, P. (2014) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0368-8 · Cited by 98

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(2002) · doi:10.1007/0-387-21529-8_16 · Cited by 98

Clock polymorphism in Pacific salmon: evidence for variable selection along a latitudinal gradient

O'Malley, K. G., Ford, M. J., & Hard, J. J. (2010) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2010.0762 · Cited by 97

Tree-based ensemble methods for sensitivity analysis of environmental models: A performance comparison with Sobol and Morris techniques

Jaxa-Rozen, M., & Kwakkel, J. (2018) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2018.06.011 · Cited by 91

Apolipoprotein E epsilon 4 genotype and a physically active lifestyle in late life: analysis of gene-environment interaction for the risk of dementia and Alzheimer’s disease dementia

Luck, T., Riedel-Heller, S. G., Luppa, M., Wiese, B., Köhler, M., Jessen, F., Bickel, H., Weyerer, S., Pentzek, M., König, H.-H., et al. (2014) · Psychological Medicine · doi:10.1017/s0033291713001918 · Cited by 93

Molecular Insights from Conformational Ensembles via Machine Learning

Fleetwood, O., Kasimova, M. A., Westerlund, A. M., & Delemotte, L. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2019.12.016 · Cited by 93

Unexplained mortality differences between septic shock trials: a systematic analysis of population characteristics and control-group mortality rates

de Grooth, H.-J., Postema, J., Loer, S. A., Parienti, J.-J., Oudemans-van Straaten, H. M., & Girbes, A. R. (2018) · Intensive Care Medicine · doi:10.1007/s00134-018-5134-8 · Cited by 92

Wildfire and Spatial Patterns in Forests in Northwestern Mexico: The United States Wishes It Had Similar Fire Problems

Stephens, S. L., Fry, D. L., & Franco-Vizcaíno, E. (2008) · Ecology and Society · doi:10.5751/es-02380-130210 · Cited by 91

Detection of Cyber-attacks to indoor real time localization systems for autonomous robots

Guerrero-Higueras, Á. M., DeCastro-García, N., & Matellán, V. (2018) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2017.10.006 · Cited by 91

Why you should stop predicting customer churn and start using uplift models

Devriendt, F., Berrevoets, J., & Verbeke, W. (2021) · Information Sciences · doi:10.1016/j.ins.2019.12.075 · Cited by 90

Promoting physical activity among cancer survivors: Meta-analysis and meta-CART analysis of randomized controlled trials.

Sheeran, P., Abraham, C., Jones, K., Villegas, M. E., Avishai, A., Symes, Y. R., Ellinger, H., Miles, E., Gates, K. M., Wright, C. E., et al. (2019) · Health Psychology · doi:10.1037/hea0000712 · Cited by 90

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(2012) · Cited by 90

Detecting outlying properties of exceptional objects

Angiulli, F., Fassetti, F., & Palopoli, L. (2009) · ACM Transactions on Database Systems · doi:10.1145/1508857.1508864 · Cited by 88

Assessing bank soundness with classification techniques

Ioannidis, C., Pasiouras, F., & Zopounidis, C. (2010) · Omega · doi:10.1016/j.omega.2009.10.009 · Cited by 89

HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning

Schelter, S., Grafberger, S., & Dunning, T. (2021) · Proceedings of the 2021 International Conference on Management of Data · doi:10.1145/3448016.3457239 · Cited by 89

Predicting anxiety state using smartphone-based passive sensing

Fukazawa, Y., Ito, T., Okimura, T., Yamashita, Y., Maeda, T., & Ota, J. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103151 · Cited by 81

Housing arrangement and vegetation factors associated with single-family home survival in the 2018 Camp Fire, California

Knapp, E. E., Valachovic, Y. S., Quarles, S. L., & Johnson, N. G. (2021) · Fire Ecology · doi:10.1186/s42408-021-00117-0 · Cited by 84

California's regulatory forest carbon market: Viability for northeast landowners

Kerchner, C. D., & Keeton, W. S. (2015) · Forest Policy and Economics · doi:10.1016/j.forpol.2014.09.005 · Cited by 79

Understanding patient outcomes after acute respiratory distress syndrome: identifying subtypes of physical, cognitive and mental health outcomes

Brown, S. M., Wilson, E. L., Presson, A. P., Dinglas, V. D., Greene, T., Hopkins, R. O., & Needham, D. M. (2017) · Thorax · doi:10.1136/thoraxjnl-2017-210337 · Cited by 82

Growing random forest on deep convolutional neural networks for scene categorization

Bai, S. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.10.038 · Cited by 81

Geographic and ecologic heterogeneity in elimination thresholds for the major vector-borne helminthic disease, lymphatic filariasis

Gambhir, M., Bockarie, M., Tisch, D., Kazura, J., Remais, J., Spear, R., & Michael, E. (2010) · BMC Biology · doi:10.1186/1741-7007-8-22 · Cited by 78

Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients

Massé, F., Gonzenbach, R. R., Arami, A., Paraschiv-Ionescu, A., Luft, A. R., & Aminian, K. (2015) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-015-0060-2 · Cited by 77

Characterizing user engagement with health app data: a data mining approach

Serrano, K. J., Coa, K. I., Yu, M., Wolff-Hughes, D. L., & Atienza, A. A. (2017) · Translational Behavioral Medicine · doi:10.1007/s13142-017-0508-y · Cited by 67

Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer

Toyama, Y., Hotta, M., Motoi, F., Takanami, K., Minamimoto, R., & Takase, K. (2020) · Scientific Reports · doi:10.1038/s41598-020-73237-3 · Cited by 75