Classification and Regression Trees.

Early indicators of scientific impact: Predicting citations with altmetrics

Akella, A. P., Alhoori, H., Kondamudi, P. R., Freeman, C., & Zhou, H. (2021) · Journal of Informetrics · doi:10.1016/j.joi.2020.101128 · Cited by 74

Correlation between normalized difference vegetation index and malaria in a subtropical rain forest undergoing rapid anthropogenic alteration

Wayant, N. M., Maldonado, D., Rojas de Arias, A., Cousiño, B., & Goodin, D. G. (2010) · Geospatial health · doi:10.4081/gh.2010.199 · Cited by 73

Prototype of an adaptive disruption predictor for JET based on fuzzy logic and regression trees

Murari, A., Vagliasindi, G., Arena, P., Fortuna, L., Barana, O., & Johnson, M. (2008) · Nuclear Fusion · doi:10.1088/0029-5515/48/3/035010 · Cited by 72

The role of affect-driven impulsivity in gambling cognitions: a convenience-sample study with a spanish version of the gambling-related cognitions scale

Del Prete, F., Steward, T., Navas, J. F., Fernández-Aranda, F., Jiménez-Murcia, S., Oei, T. P. S., & Perales, J. C. (2017) · Journal of Behavioral Addictions · doi:10.1556/2006.6.2017.001 · Cited by 70

Nutrition education is positively associated with substance abuse treatment program outcomes

Grant, L. P., Haughton, B., & Sachan, D. S. (2004) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2004.01.008 · Cited by 70

Cognitive Analytics

Gudivada, V. N., Irfan, M. T., Fathi, E., & Rao, D. L. (2016) · Handbook of Statistics · doi:10.1016/bs.host.2016.07.010 · Cited by 70

Satisfaction with and usage of a hand neuroprosthesis

Wuolle, K. S., Van Doren, C. L., Bryden, A. M., Hunter Peckham, P., Keith, M. W., Kilgore, K. L., & Grill, J. H. (1999) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/s0003-9993(99)90123-5 · Cited by 70

A machine learning approach to improve congestion control over wireless computer networks

Geurts, P., El Khayat, I., & Leduc, G. (2004) · Fourth IEEE International Conference on Data Mining (ICDM'04) · doi:10.1109/icdm.2004.10063 · Cited by 68

Propensity score adjustment using machine learning classification algorithms to control selection bias in online surveys

Ferri-García, R., & Rueda, M. D. M. (2020) · PLOS ONE · doi:10.1371/journal.pone.0231500 · Cited by 67

A formwork method selection model based on boosted decision trees in tall building construction

Shin, Y., Kim, T., Cho, H., & Kang, K.-I. (2012) · Automation in Construction · doi:10.1016/j.autcon.2011.12.007 · Cited by 66

Biodiversity among Lactobacillus helveticus Strains Isolated from Different Natural Whey Starter Cultures as Revealed by Classification Trees

Gatti, M., Trivisano, C., Fabrizi, E., Neviani, E., & Gardini, F. (2004) · Applied and Environmental Microbiology · doi:10.1128/aem.70.1.182-190.2004 · Cited by 66

Confident Interpretation of Bayesian Decision Tree Ensembles for Clinical Applications

Schetinin, V., Fieldsend, J. E., Partridge, D., Coats, T. J., Krzanowski, W. J., Everson, R. M., Bailey, T. C., & Hernandez, A. (2007) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2006.880553 · Cited by 64

Including information about co-morbidity in estimates of disease burden: results from the World Health Organization World Mental Health Surveys

Alonso, J., Vilagut, G., Chatterji, S., Heeringa, S., Schoenbaum, M., Bedirhan Üstün, T., Rojas-Farreras, S., Angermeyer, M., Bromet, E., Bruffaerts, R., et al. (2011) · Psychological Medicine · doi:10.1017/s0033291710001212 · Cited by 61

Citizens as consumers: Profiling e-government services’ users in Egypt via data mining techniques

Mostafa, M. M., & El-Masry, A. A. (2013) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2013.03.007 · Cited by 60

Investigating the role of feedstock properties and process conditions on products formed during the hydrothermal carbonization of organics using regression techniques

Li, L., Flora, J. R. V., Caicedo, J. M., & Berge, N. D. (2015) · Bioresource Technology · doi:10.1016/j.biortech.2015.03.054 · Cited by 57

Quantifying the sensitivity of feedstock properties and process conditions on hydrochar yield, carbon content, and energy content

Li, L., Wang, Y., Xu, J., Flora, J. R. V., Hoque, S., & Berge, N. D. (2018) · Bioresource Technology · doi:10.1016/j.biortech.2018.04.066 · Cited by 57

Random Forests for mapping and analysis of microkinetics models

Partopour, B., Paffenroth, R. C., & Dixon, A. G. (2018) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2018.04.019 · Cited by 57

Covariate selection from telematics car driving data

Wüthrich, M. V. (2017) · European Actuarial Journal · doi:10.1007/s13385-017-0149-z · Cited by 57

Using Decision Tree Analysis to Identify Risk Factors for Relapse to Smoking

Piper, M. E., Loh, W.-Y., Smith, S. S., Japuntich, S. J., & Baker, T. B. (2011) · Substance Use & Misuse · doi:10.3109/10826081003682222 · Cited by 53

Higher-order spectral analysis of spontaneous speech signals in Alzheimer’s disease

Nasrolahzadeh, M., Mohammadpoory, Z., & Haddadnia, J. (2018) · Cognitive Neurodynamics · doi:10.1007/s11571-018-9499-8 · Cited by 51

Automatic Subtyping of Individuals with Primary Progressive Aphasia

Themistocleous, C., Ficek, B., Webster, K., den Ouden, D.-B., Hillis, A. E., & Tsapkini, K. (2021) · Journal of Alzheimer’s Disease · doi:10.3233/jad-201101 · Cited by 51

High Frequency Remote Monitoring of Parkinson's Disease via Smartphone: Platform Overview and Medication Response Detection

Zhan, A., Little, M. A., Harris, D. A., Abiola, S. O., Dorsey, E. R., Saria, S., & Terzis, A. (2016) · arXiv · doi:10.48550/arxiv.1601.00960 · Cited by 50

Fast data selection for SVM training using ensemble margin

Guo, L., & Boukir, S. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.08.003 · Cited by 49

Group additive regression models for genomic data analysis

Luan, Y., & Li, H. (2008) · Biostatistics · doi:10.1093/biostatistics/kxm015 · Cited by 48

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Fraccaro, P., Nicolo, M., Bonetto, M., Giacomini, M., Weller, P., Traverso, C. E., Prosperi, M., & OSullivan, D. (2015) · BMC Ophthalmology · doi:10.1186/1471-2415-15-10 · Cited by 48

A decision support framework to implement optimal personalized marketing interventions

Guelman, L., Guillén, M., & Pérez-Marín, A. M. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.01.010 · Cited by 48

Prediction of galaxy halo masses in SDSS DR7 via a machine learning approach

Calderon, V. F., & Berlind, A. A. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2775 · Cited by 48

Predictive Factors of Social Functioning in Patients with Schizophrenia: Exploration for the Best Combination of Variables Using Data Mining

Bae, S.-M., Lee, S.-H., Park, Y.-M., Hyun, M.-H., & Yoon, H. (2010) · Psychiatry Investigation · doi:10.4306/pi.2010.7.2.93 · Cited by 46

SMARTer Approach to Personalizing Intervention for Children With Autism Spectrum Disorder

Kasari, C., Sturm, A., & Shih, W. (2018) · Journal of Speech, Language, and Hearing Research · doi:10.1044/2018_jslhr-l-rsaut-18-0029 · Cited by 46

Identification of New Genetic Regions More Prevalent in Nontypeable Haemophilus influenzae Otitis Media Strains than in Throat Strains

Xie, J., Juliao, P. C., Gilsdorf, J. R., Ghosh, D., Patel, M., & Marrs, C. F. (2006) · Journal of Clinical Microbiology · doi:10.1128/jcm.01331-06 · Cited by 44

Complex interactions shaping aspen dynamics in the Greater Yellowstone Ecosystem

Brown, K., Hansen, A. J., Keane, R. E., & Graumlich, L. J. (2006) · Landscape Ecology · doi:10.1007/s10980-005-6190-3 · Cited by 43

An Exploratory Analysis of Criteria for the Metabolic Syndrome and Its Prediction of Long-term Cardiovascular Outcomes

Girman, C. J., Dekker, J. M., Rhodes, T., Nijpels, G., Stehouwer, C. D. A., Bouter, L. M., & Heine, R. J. (2005) · American Journal of Epidemiology · doi:10.1093/aje/kwi229 · Cited by 41

Predicting employee absenteeism for cost effective interventions

Lawrance, N., Petrides, G., & Guerry, M.-A. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113539 · Cited by 40

Plasma proteome and metabolome characterization of an experimental human thyrotoxicosis model

Pietzner, M., Engelmann, B., Kacprowski, T., Golchert, J., Dirk, A.-L., Hammer, E., Iwen, K. A., Nauck, M., Wallaschofski, H., Führer, D., et al. (2017) · BMC Medicine · doi:10.1186/s12916-016-0770-8 · Cited by 40

Clustering Suicide Attempters: Impulsive-Ambivalent, Well-Planned, or Frequent

Lopez-Castroman, J., Nogue, E., Guillaume, S., Picot, M. C., & Courtet, P. (2016) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.15m09882 · Cited by 38

Enhancement of TCP over wired/wireless networks with packet loss classifiers inferred by supervised learning

El Khayat, I., Geurts, P., & Leduc, G. (2010) · Wireless Networks · doi:10.1007/s11276-008-0129-y · Cited by 34

Spatially governed climate factors dominate management in determining the quantity and distribution of soil organic carbon in dryland agricultural systems

Hoyle, F. C., O’Leary, R. A., & Murphy, D. V. (2016) · Scientific Reports · doi:10.1038/srep31468 · Cited by 35

Linking surface hydrodynamics to planktonic ecosystem: the case study of the ichthyoplanktonic assemblages in the Central Mediterranean Sea

Cuttitta, A., Torri, M., Zarrad, R., Zgozi, S., Jarboui, O., Quinci, E. M., Hamza, M., Abdulfatah, E., Haddoud, D., El Turki, A., et al. (2018) · Hydrobiologia · doi:10.1007/s10750-017-3483-x · Cited by 33

Prediction of 3-year clinical course in CADASIL

Jouvent, E., Duchesnay, E., Hadj-Selem, F., De Guio, F., Mangin, J.-F., Hervé, D., Duering, M., Ropele, S., Schmidt, R., Dichgans, M., et al. (2016) · Neurology · doi:10.1212/wnl.0000000000003252 · Cited by 30

The Coffee Paradox in Stroke: Increased Consumption Linked with Fewer Strokes

Liebeskind, D. S., Sanossian, N., Fu, K. A., Wang, H.-J., & Arab, L. (2016) · Nutritional Neuroscience · doi:10.1179/1476830515y.0000000035 · Cited by 30

Measuring, Characterizing, and Detecting Facebook Like Farms

Ikram, M., Onwuzurike, L., Farooqi, S., Cristofaro, E. D., Friedman, A., Jourjon, G., Kaafar, M. A., & Shafiq, M. Z. (2017) · ACM Transactions on Privacy and Security · doi:10.1145/3121134 · Cited by 27

Can Web Search Queries Predict Prices Change on the Real Estate Market?

Rizun, N., & Baj-Rogowska, A. (2021) · IEEE Access · doi:10.1109/access.2021.3077860 · Cited by 26

Availability and capacity of substance abuse programs in correctional settings: A classification and regression tree analysis

Taxman, F. S., & Kitsantas, P. (2009) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2009.01.008 · Cited by 25

Optimizing the recency-relevance-diversity trade-offs in non-personalized news recommendations

Chakraborty, A., Ghosh, S., Ganguly, N., & Gummadi, K. P. (2019) · Information Retrieval Journal · doi:10.1007/s10791-019-09351-2 · Cited by 24

Stochastic gradient boosting frequency-severity model of insurance claims

Su, X., & Bai, M. (2020) · PLOS ONE · doi:10.1371/journal.pone.0238000 · Cited by 24

Differential protein expression profiles between Plasmodium falciparum parasites isolated from subjects presenting with pregnancy-associated malaria and uncomplicated malaria in Benin

Bertin, G. I., Sabbagh, A., Guillonneau, F., Jafari-Guemouri, S., Ezinmegnon, S., Federici, C., Hounkpatin, B., Fievet, N., & Deloron, P. (2013) · The Journal of Infectious Diseases · doi:10.1093/infdis/jit377 · Cited by 21

Antiphospholipid autoantibodies as blood biomarkers for detection of early stage Alzheimer’s disease

McIntyre, J. A., Ramsey, C. J., Gitter, B. D., Saykin, A. J., Wagenknecht, D. R., Hyslop, P. A., & Initiative, F. T. A. D. N. (2015) · Autoimmunity · doi:10.3109/08916934.2015.1008464 · Cited by 22

Mining Free-Text Medical Notes for Suicide Risk Assessment

Adamou, M., Antoniou, G., Greasidou, E., Lagani, V., Charonyktakis, P., & Tsamardinos, I. (2018) · Proceedings of the 10th Hellenic Conference on Artificial Intelligence · doi:10.1145/3200947.3201020 · Cited by 22

Age-related differences in cerebral blood flow and cortical thickness with an application to age prediction

MacDonald, M. E., Williams, R. J., Rajashekar, D., Stafford, R. B., Hanganu, A., Sun, H., Berman, A. J. L., McCreary, C. R., Frayne, R., Forkert, N. D., et al. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.06.019 · Cited by 21

Active learning with missing values considering imputation uncertainty

Han, J., & Kang, S. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107079 · Cited by 21

Improving TCP in Wireless Networks with an Adaptive Machine-Learnt Classifier of Packet Loss Causes

El Khayat, I., Geurts, P., & Leduc, G. (2005) · Lecture Notes in Computer Science · doi:10.1007/11422778_44 · Cited by 20

City adjustments as the main factor of success of urban and peri-urban farms–empirical evidence from the Ruhr metropolis

Sroka, W., Pölling, B., & Mergenthaler, M. (2019) · NJAS: Wageningen Journal of Life Sciences · doi:10.1016/j.njas.2019.04.005 · Cited by 19

Uncovering social-contextual and individual mental health factors associated with violence via computational inference

Santamaría-García, H., Baez, S., Aponte-Canencio, D. M., Pasciarello, G. O., Donnelly-Kehoe, P. A., Maggiotti, G., Matallana, D., Hesse, E., Neely, A., Zapata, J. G., et al. (2021) · Patterns · doi:10.1016/j.patter.2020.100176 · Cited by 18

An Instruction Set Architecture for Machine Learning

Chen, Y., Lan, H., Du, Z., Liu, S., Tao, J., Han, D., Luo, T., Guo, Q., Li, L., Xie, Y., et al. (2018) · ACM Transactions on Computer Systems · doi:10.1145/3331469 · Cited by 14

Baseline predictors of treatment outcome in Internet-based alcohol interventions: a recursive partitioning analysis alongside a randomized trial

Blankers, M., Koeter, M. W., & Schippers, G. M. (2013) · BMC Public Health · doi:10.1186/1471-2458-13-455 · Cited by 14

Modos de vida entre pessoas que tiveram câncer no Brasil em 2013

Silva, G. A. E., Rezende, L. F. M. D., Gomes, F. D. S., Souza Júnior, P. R. B. D., Szwarcwald, C. L., & Eluf Neto, J. (2016) · Ciência & Saúde Coletiva · doi:10.1590/1413-81232015211.24722015 · Cited by 13

Deep-learning-based precipitation observation quality control

Sha, Y., Gagne, D. J., West, G., & Stull, R. (2021) · Journal of Atmospheric and Oceanic Technology · doi:10.1175/jtech-d-20-0081.1 · Cited by 13

A novel machine learning-derived decision tree including uPA/PAI-1 for breast cancer care

Reix, N., Lodi, M., Jankowski, S., Molière, S., Luporsi, E., Leblanc, S., Scheer, L., Ibnouhsein, I., Benabu, J.-C., Gabriele, V., et al. (2019) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2018-1065 · Cited by 10

Machine-learnt versus analytical models of TCP throughput

El Khayat, I., Geurts, P., & Leduc, G. (2007) · Computer Networks · doi:10.1016/j.comnet.2006.11.017 · Cited by 10

Using time domain and pearson’s correlation to predict attention focus in autistic spectrum disorder from EEG P300 components

Adama, V. S., Schindler, B., & Schmid, T. (2020) · IFMBE Proceedings · doi:10.1007/978-3-030-31635-8_230 · Cited by 10

Methods of Computational Intelligence in the Context of Quality Assurance in Foundry Products

Rojek, G., Regulski, K., Wilk-Kołodziejczyk, D., Kluska-Nawarecka, S., Jaśkowiec, K., & Smolarek-Grzyb, A. (2016) · Archives of Foundry Engineering · doi:10.1515/afe-2016-0018 · Cited by 8

Molecular insights from conformational ensembles via machine learning

(2019) · doi:10.1101/695254 · Cited by 6

A statistical method (cross-validation) for bone loss region detection after spaceflight

Zhao, Q., Li, W., Li, C., Chu, P. W., Kornak, J., Lang, T. F., Fang, J., & Lu, Y. (2010) · Australasian Physical & Engineering Sciences in Medicine · doi:10.1007/s13246-010-0024-6 · Cited by 5

Lightweight Mobile Automated Assistant-to-physician for Global Lower-resource Areas

Zhang, C., Zhang, H., Khan, A., Kim, T., Omoleye, O., Abiona, O., Lehman, A., Olopade, C. O., Olopade, O. I., Lopes, P., et al. (2021) · doi:10.1101/2021.11.01.21265487 · Cited by 4

Detection of articulation disorders using Empirical Mode Decomposition and neural networks

Georgoulas, G., Georgopoulos, V. C., Stylios, G. D., & Stylios, C. D. (2009) · 2009 International Joint Conference on Neural Networks · doi:10.1109/ijcnn.2009.5178935 · Cited by 3

Modeling Energy Consumption Based on Resource Utilization

Povoa, L. V., Marcondes, C., & Senger, H. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-24289-3_18 · Cited by 2