Classification and Regression Trees.

An evaluation of methods for modelling species distributions

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Patterns and uncertainties of species' range shifts under climate change

Thuiller, W. (2004) · Global Change Biology · doi:10.1111/j.1365-2486.2004.00859.x · Cited by 873

Risk factors for binge eating onset in adolescent girls: A 2-year prospective investigation.

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A review of feature selection methods on synthetic data

Bolón-Canedo, V., Sánchez-Maroño, N., & Alonso-Betanzos, A. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-012-0487-8 · Cited by 830

Machine Learning Methods Without Tears: A Primer for Ecologists

Olden, J. D., Lawler, J. J., & Poff, N. L. (2008) · The Quarterly Review of Biology · doi:10.1086/587826 · Cited by 823

An Adaptive Estimation of Dimension Reduction Space

Xia, Y., Tong, H., Li, W. K., & Zhu, L.-X. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.03411 · Cited by 820

Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints

van der Ploeg, T., Austin, P. C., & Steyerberg, E. W. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-137 · Cited by 809

Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research.

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Learning and evaluating classifiers under sample selection bias

Zadrozny, B. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015425 · Cited by 800

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

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Predicting outcome in severe ulcerative colitis.

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Consumer credit-risk models via machine-learning algorithms

Khandani, A. E., Kim, A. J., & Lo, A. W. (2010) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2010.06.001 · Cited by 864

Activity Classification Using Realistic Data From Wearable Sensors

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Ambiguity and self-evaluation: The role of idiosyncratic trait definitions in self-serving assessments of ability.

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C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993

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A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition

Ben Taieb, S., Bontempi, G., Atiya, A. F., & Sorjamaa, A. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.039 · Cited by 734

Decision trees: An overview and their use in medicine

Podgorelec, V., Kokol, P., Stiglic, B., & Rozman, I. (2002) · Journal of Medical Systems · doi:10.1023/a:1016409317640 · Cited by 736

Random forest regression for online capacity estimation of lithium-ion batteries

Li, Y., Zou, C., Berecibar, M., Nanini-Maury, E., Chan, J. C.-W., van den Bossche, P., Van Mierlo, J., & Omar, N. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.09.182 · Cited by 727

Harnessing context sensing to develop a mobile intervention for depression

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Untitled work

(2001) · Cited by 683

Untitled work

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Supervised neural networks for the classification of structures

Sperduti, A., & Starita, A. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.572108 · Cited by 666

Manipulating and Measuring Model Interpretability

Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Wortman Vaughan, J. W., & Wallach, H. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445315 · Cited by 728

Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights

Pasolli, E., Truong, D. T., Malik, F., Waldron, L., & Segata, N. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004977 · Cited by 659

A new method for detecting and interpreting biodiversity and ecological community thresholds: Threshold Indicator Taxa ANalysis (TITAN)

Baker, M. E., & King, R. S. (2010) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2009.00007.x · Cited by 658

Journal prestige publication bias, and other characteristics associated with citation of published studies in peer-reviewed journals

Callaham, M. (2002) · JAMA · doi:10.1001/jama.287.21.2847 · Cited by 644

Machine learning and its applications to biology

Tarca, A. L., Carey, V. J., Chen, X.-W., Romero, R., & Drăghici, S. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030116 · Cited by 656

Decreased β-Amyloid1-42and Increased Tau Levels in Cerebrospinal Fluid of Patients With Alzheimer Disease

Sunderland, T., Linker, G., Mirza, N., Putnam, K. T., Friedman, D. L., Kimmel, L. H., Bergeson, J., Manetti, G. J., Zimmermann, M., Tang, B., et al. (2003) · JAMA · doi:10.1001/jama.289.16.2094 · Cited by 656

A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods

BURMAN, P. (1989) · Biometrika · doi:10.1093/biomet/76.3.503 · Cited by 634

Wedgelets: nearly minimax estimation of edges

Donoho, D. L. (1999) · The Annals of Statistics · doi:10.1214/aos/1018031261 · Cited by 638

Global estimation of burned area using MODIS active fire observations

Giglio, L., van der Werf, G. R., Randerson, J. T., Collatz, G. J., & Kasibhatla, P. (2006) · Atmospheric Chemistry and Physics · doi:10.5194/acp-6-957-2006 · Cited by 635

Predicting stock market index using fusion of machine learning techniques

Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.10.031 · Cited by 608

Twenty Years of Mixture of Experts

Yuksel, S. E., Wilson, J. N., & Gader, P. D. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2200299 · Cited by 606

Woody cover in African savannas: the role of resources, fire and herbivory.

Sankaran, M., Ratnam, J., & Hanan, N. (2008) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2007.00360.x · Cited by 589

Let's put garbage-can regressions and garbage-can probits where they belong

Achen, C. H. (2005) · Conflict Management and Peace Science · doi:10.1080/07388940500339167 · Cited by 576

Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts

Zhao, Z., Resnick, P., & Mei, Q. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741637 · Cited by 583

Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation

Tu, Z., & Bai, X. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.186 · Cited by 578

Neonatal cytokines and coagulation factors in children with cerebral palsy

Nelson, K. B., Dambrosia, J. M., Grether, J. K., & Phillips, T. M. (1998) · Annals of Neurology · doi:10.1002/ana.410440413 · Cited by 549

Consistent Individualized Feature Attribution for Tree Ensembles

Lundberg, S. M., Erion, G. G., & Lee, S.-I. (2018) · arXiv · doi:10.48550/arxiv.1802.03888 · Cited by 1,430

Deciphering the distribution of the savanna biome

Lehmann, C. E. R., Archibald, S. A., Hoffmann, W. A., & Bond, W. J. (2011) · New Phytologist · doi:10.1111/j.1469-8137.2011.03689.x · Cited by 523

Grip Strength Cutpoints for the Identification of Clinically Relevant Weakness

Alley, D. E., Shardell, M. D., Peters, K. W., McLean, R. R., Dam, T.-T. L., Kenny, A. M., Fragala, M. S., Harris, T. B., Kiel, D. P., Guralnik, J. M., et al. (2014) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/glu011 · Cited by 535

Statistical decision-tree models for parsing

Magerman, D. M. (1995) · Proceedings of the 33rd annual meeting on Association for Computational Linguistics - · doi:10.3115/981658.981695 · Cited by 567

Effectiveness of inpatient dialectical behavioral therapy for borderline personality disorder: a controlled trial

Bohus, M., Haaf, B., Simms, T., Limberger, M. F., Schmahl, C., Unckel, C., Lieb, K., & Linehan, M. M. (2004) · Behaviour Research and Therapy · doi:10.1016/s0005-7967(03)00174-8 · Cited by 526

Tree Induction for Probability-Based Ranking

Provost, F., & Domingos, P. (2003) · Machine Learning · doi:10.1023/a:1024099825458 · Cited by 541

Boosted decision trees as an alternative to artificial neural networks for particle identification

Roe, B. P., Yang, H.-J., Zhu, J., Liu, Y., Stancu, I., & McGregor, G. (2005) · Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment · doi:10.1016/j.nima.2004.12.018 · Cited by 534

FOIL: A midterm report

Quinlan, J. R., & Cameron-Jones, R. M. (1993) · Lecture Notes in Computer Science · doi:10.1007/3-540-56602-3_124 · Cited by 528

A general approach for predicting the behavior of the Supreme Court of the United States

Katz, D. M., Bommarito, M. J., & Blackman, J. (2017) · PLOS ONE · doi:10.1371/journal.pone.0174698 · Cited by 528

A Multisite Study of the Clinical Diagnosis of Different Autism Spectrum Disorders

Lord, C. (2012) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2011.148 · Cited by 516

Blended learning: A dangerous idea?

Moskal, P., Dziuban, C., & Hartman, J. (2013) · The Internet and Higher Education · doi:10.1016/j.iheduc.2012.12.001 · Cited by 494

On the Class Imbalance Problem

Guo, X., Yin, Y., Dong, C., Yang, G., & Zhou, G. (2008) · 2008 Fourth International Conference on Natural Computation · doi:10.1109/icnc.2008.871 · Cited by 495

Turning telecommunications call details to churn prediction: a data mining approach

Wei, C.-P., & Chiu, I.-T. (2002) · Expert Systems with Applications · doi:10.1016/s0957-4174(02)00030-1 · Cited by 460

New insights into churn prediction in the telecommunication sector: a profit driven data mining approach

Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012) · European Journal of Operational Research · doi:10.1016/j.ejor.2011.09.031 · Cited by 462

A new measure of prognostic separation in survival data

Royston, P., & Sauerbrei, W. (2004) · Statistics in Medicine · doi:10.1002/sim.1621 · Cited by 450

Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo

Ding, C., Cao, X. (., & Næss, P. (2018) · Transportation Research Part A: Policy and Practice · doi:10.1016/j.tra.2018.02.009 · Cited by 446

High Cerebrospinal Fluid Tau and Low Amyloid β42 Levels in the Clinical Diagnosis of Alzheimer Disease and Relation to Apolipoprotein E Genotype

Galasko, D., Chang, L., Motter, R., Clark, C. M., Kaye, J., Knopman, D., Thomas, R., Kholodenko, D., Schenk, D., Lieberburg, I., et al. (1998) · Archives of Neurology · doi:10.1001/archneur.55.7.937 · Cited by 442

Overweight Status and Eating Patterns Among Adolescents: Where Do Youths Stand in Comparison With the Healthy People 2010 Objectives?

Neumark-Sztainer, D., Story, M., Hannan, P. J., & Croll, J. (2002) · American Journal of Public Health · doi:10.2105/ajph.92.5.844 · Cited by 440

Measurement of Phosphorylated Tau Epitopes in the Differential Diagnosisof Alzheimer Disease: A Comparative Cerebrospinal Fluid Study

Hampel, H., Buerger, K., Zinkowski, R., Teipel, S. J., Goernitz, A., Andreasen, N., Sjoegren, M., DeBernardis, J., Kerkman, D., Ishiguro, K., et al. (2004) · Archives of General Psychiatry · doi:10.1001/archpsyc.61.1.95 · Cited by 434

A comparative study of machine learning classifiers for modeling travel mode choice

Hagenauer, J., & Helbich, M. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.01.057 · Cited by 424

An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models

Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., & Baesens, B. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.12.003 · Cited by 425

Bagging and Boosting Classification Trees to Predict Churn

Lemmens, A., & Croux, C. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.2.276 · Cited by 411

The Practical Implementation of Bayesian Model Selection

Chipman, H., George, E. I., & McCulloch, R. E. (2001) · Institute of Mathematical Statistics Lecture Notes - Monograph Series · doi:10.1214/lnms/1215540964 · Cited by 416

Time series shapelets: a novel technique that allows accurate, interpretable and fast classification

Ye, L., & Keogh, E. (2011) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0179-5 · Cited by 406

Evaluating Prediction Rules for t -Year Survivors With Censored Regression Models

Uno, H., Cai, T., Tian, L., & Wei, L. J. (2007) · Journal of the American Statistical Association · doi:10.1198/016214507000000149 · Cited by 413

Decline in Lung Function in the Busselton Health Study

James, A. L., Palmer, L. J., Kicic, E., Maxwell, P. S., Lagan, S. E., Ryan, G. F., & Musk, A. W. (2005) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200402-230oc · Cited by 397

Methotrexate-Induced Neurotoxicity and Leukoencephalopathy in Childhood Acute Lymphoblastic Leukemia

Bhojwani, D., Sabin, N. D., Pei, D., Yang, J. J., Khan, R. B., Panetta, J. C., Krull, K. R., Inaba, H., Rubnitz, J. E., Metzger, M. L., et al. (2014) · Journal of Clinical Oncology · doi:10.1200/jco.2013.53.0808 · Cited by 400

Business intelligence: data mining and optimization for decision making

Vercellis, C., Ahmadi, A., & Mohebi, A. (2016) · doi:10.5281/zenodo.13983826 · Cited by 398

Fake news detection within online social media using supervised artificial intelligence algorithms

Ozbay, F. A., & Alatas, B. (2020) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.123174 · Cited by 394

Bayesian approach for neural networks—review and case studies

Lampinen, J., & Vehtari, A. (2001) · Neural Networks · doi:10.1016/s0893-6080(00)00098-8 · Cited by 392

Identification of Individuals With Insulin Resistance Using Routine Clinical Measurements

Stern, S. E., Williams, K., Ferrannini, E., DeFronzo, R. A., Bogardus, C., & Stern, M. P. (2005) · Diabetes · doi:10.2337/diabetes.54.2.333 · Cited by 384

Peak oxygen intake and cardiac mortality in women referred for cardiac rehabilitation

Kavanagh, T., Mertens, D. J., Hamm, L. F., Beyene, J., Kennedy, J., Corey, P., & Shephard, R. J. (2003) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2003.07.028 · Cited by 380

Incremental on-line learning: A review and comparison of state of the art algorithms

Losing, V., Hammer, B., & Wersing, H. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.06.084 · Cited by 382

Cutpoints for low appendicular lean mass that identify older adults with clinically significant weakness

Cawthon, P. M., Peters, K. W., Shardell, M. D., McLean, R. R., Dam, T.-T. L., Kenny, A. M., Fragala, M. S., Harris, T. B., Kiel, D. P., Guralnik, J. M., et al. (2014) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/glu023 · Cited by 388

Using Model Trees for Classification

Frank, E., Wang, Y., Inglis, S., Holmes, G., & Witten, I. H. (1998) · Machine Learning · doi:10.1023/a:1007421302149 · Cited by 377

The case for the inclusion of female subjects in basic science studies of pain

Mogil, J. S., & Chanda, M. L. (2005) · Pain · doi:10.1016/j.pain.2005.06.020 · Cited by 376

Relational Reinforcement Learning

Džeroski, S., De Raedt, L., & Driessens, K. (2001) · Machine Learning · doi:10.1023/a:1007694015589 · Cited by 371

Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus

Barakat, N., Bradley, A. P., & Barakat, M. N. H. (2010) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2039485 · Cited by 367

Random Forests for Big Data

Genuer, R., Poggi, J.-M., Tuleau-Malot, C., & Villa-Vialaneix, N. (2017) · Big Data Research · doi:10.1016/j.bdr.2017.07.003 · Cited by 367

Defining insulin resistance from hyperinsulinemic-euglycemic clamps

Tam, C. S., Xie, W., Johnson, W. D., Cefalu, W. T., Redman, L. M., & Ravussin, E. (2012) · Diabetes Care · doi:10.2337/dc11-2339 · Cited by 364

Data analysis strategies for targeted and untargeted LC-MS metabolomic studies: Overview and workflow

Gorrochategui, E., Jaumot, J., Lacorte, S., & Tauler, R. (2016) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2016.07.004 · Cited by 352

Short-Term Prediction of Wind Farm Power: A Data Mining Approach

Kusiak, A., Zheng, H., & Song, Z. (2009) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2008.2006552 · Cited by 339

Human Activity Helps Prey Win the Predator-Prey Space Race

Muhly, T. B., Semeniuk, C., Massolo, A., Hickman, L., & Musiani, M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017050 · Cited by 337

Simplifying decision trees: A survey

BRESLOW, L. A., & AHA, D. W. (1997) · The Knowledge Engineering Review · doi:10.1017/s0269888997000015 · Cited by 337

A Pearson’s correlation coefficient based decision tree and its parallel implementation

Mu, Y., Liu, X., & Wang, L. (2018) · Information Sciences · doi:10.1016/j.ins.2017.12.059 · Cited by 336

Global and regional analysis of climate and human drivers of wildfire

Aldersley, A., Murray, S. J., & Cornell, S. E. (2011) · Science of The Total Environment · doi:10.1016/j.scitotenv.2011.05.032 · Cited by 311

On the use of MapReduce for imbalanced big data using Random Forest

del Río, S., López, V., Benítez, J. M., & Herrera, F. (2014) · Information Sciences · doi:10.1016/j.ins.2014.03.043 · Cited by 328

Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques

Monte-Moreno, E. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2011.05.001 · Cited by 311

Deep learning algorithm predicts diabetic retinopathy progression in individual patients

Arcadu, F., Benmansour, F., Maunz, A., Willis, J., Haskova, Z., & Prunotto, M. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0172-3 · Cited by 305

A Bayesian Approach to Learning Bayesian Networks with Local Structure

Chickering, D. M., Heckerman, D. E., & Meek, C. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.1528 · Cited by 314

An empirical study of machine learning techniques for affect recognition in human–robot interaction

Rani, P., Liu, C., Sarkar, N., & Vanman, E. (2006) · Pattern Analysis and Applications · doi:10.1007/s10044-006-0025-y · Cited by 313

Clinical Prediction Rule for Identifying Children With Cerebrospinal Fluid Pleocytosis at Very Low Risk of Bacterial Meningitis

Nigrovic, L. E. (2007) · JAMA · doi:10.1001/jama.297.1.52 · Cited by 306

Fungal cell gigantism during mammalian infection

Zaragoza, O., García-Rodas, R., Nosanchuk, J. D., Cuenca-Estrella, M., Rodríguez-Tudela, J. L., & Casadevall, A. (2010) · PLoS Pathogens · doi:10.1371/journal.ppat.1000945 · Cited by 311

Overfitting Avoidance as Bias

Schaffer, C. (1993) · Machine Learning · doi:10.1023/a:1022653209073 · Cited by 310

Transaction aggregation as a strategy for credit card fraud detection

Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-008-0116-z · Cited by 337

Random Forests and Kernel Methods

Scornet, E. (2016) · IEEE Transactions on Information Theory · doi:10.1109/tit.2016.2514489 · Cited by 296

Occupancy schedules learning process through a data mining framework

D’Oca, S., & Hong, T. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2014.11.065 · Cited by 294

Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity

Anderson, B., & McGrew, D. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098163 · Cited by 295

Recent advances in surface defect inspection of industrial products using deep learning techniques

Zheng, X., Zheng, S., Kong, Y., & Chen, J. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-06592-8 · Cited by 287

A Framework for Operational Modelling of Hospital Resources

Harper, P. R. (2002) · Health Care Management Science · doi:10.1023/a:1019767900627 · Cited by 279

Managing Marketing Communications with Multichannel Customers

Thomas, J. S., & Sullivan, U. Y. (2005) · Journal of Marketing · doi:10.1509/jmkg.2005.69.4.239 · Cited by 286

Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning

Al-Hmouz, A., Shen, J., Al-Hmouz, R., & Yan, J. (2012) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2011.36 · Cited by 286