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Stochastic gradient boosting

Citations

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Cited by:

  1. Qingzhao Yu, 2011. "Weighted bagging: a modification of AdaBoost from the perspective of importance sampling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(3), pages 451-463, October.
  2. Konstantinos C. Giotopoulos & Dimitrios Michalopoulos & Gerasimos Vonitsanos & Dimitris Papadopoulos & Ioanna Giannoukou & Spyros Sioutas, 2025. "Dynamic Workload Management System in the Public Sector: A Comparative Analysis," Future Internet, MDPI, vol. 17(3), pages 1-39, March.
  3. repec:jss:jstsof:17:i02 is not listed on IDEAS
  4. Vaishnavi Bherde & Nethish Gorantala & Umashankar Balunaini, 2025. "Liquefaction susceptibility prediction using ML-based voting ensemble classifier," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(4), pages 4359-4384, March.
  5. Philippe Lauret & Mathieu David & Hugo T. C. Pedro, 2017. "Probabilistic Solar Forecasting Using Quantile Regression Models," Energies, MDPI, vol. 10(10), pages 1-17, October.
  6. Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
  7. Ünal, Cemre & Ceasu, Ioana, 2019. "A Machine Learning Approach Towards Startup Success Prediction," IRTG 1792 Discussion Papers 2019-022, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  8. Christophe Dutang & Quentin Guibert, 2021. "An explicit split point procedure in model-based trees allowing for a quick fitting of GLM trees and GLM forests," Post-Print hal-03448250, HAL.
  9. Bohdan M. Pavlyshenko, 2019. "Machine-Learning Models for Sales Time Series Forecasting," Data, MDPI, vol. 4(1), pages 1-11, January.
  10. Mansoor, Umer & Jamal, Arshad & Su, Junbiao & Sze, N.N. & Chen, Anthony, 2023. "Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations," Transport Policy, Elsevier, vol. 139(C), pages 21-38.
  11. Richard Berk & Lawrence Sherman & Geoffrey Barnes & Ellen Kurtz & Lindsay Ahlman, 2009. "Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 191-211, January.
  12. Matthew Smith & Francisco Alvarez, 2022. "Predicting Firm-Level Bankruptcy in the Spanish Economy Using Extreme Gradient Boosting," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 263-295, January.
  13. Luis Gomes & Tânia Nobre & Adélia Sousa & Fernando Rei & Nuno Guiomar, 2020. "Hyperspectral Reflectance as a Basis to Discriminate Olive Varieties—A Tool for Sustainable Crop Management," Sustainability, MDPI, vol. 12(7), pages 1-21, April.
  14. Luca Badolato & Ari Gabriel Decter-Frain & Nicolas Irons & Maria L. Miranda & Erin Walk & Elnura Zhalieva & Monica J. Alexander & Ugofilippo Basellini & Emilio Zagheni, 2023. "The limits of predicting individual-level longevity," MPIDR Working Papers WP-2023-008, Max Planck Institute for Demographic Research, Rostock, Germany.
  15. Delen, Dursun & Zolbanin, Hamed M., 2018. "The analytics paradigm in business research," Journal of Business Research, Elsevier, vol. 90(C), pages 186-195.
  16. Anna Samnioti & Vassilis Gaganis, 2023. "Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I," Energies, MDPI, vol. 16(16), pages 1-43, August.
  17. Ruby Chauhan & Shakshi Sharma & Anjali Goyal, 2023. "DENATURE: duplicate detection and type identification in open source bug repositories," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 275-292, March.
  18. Theresa Maria Rausch & Tobias Albrecht & Daniel Baier, 2022. "Beyond the beaten paths of forecasting call center arrivals: on the use of dynamic harmonic regression with predictor variables," Journal of Business Economics, Springer, vol. 92(4), pages 675-706, May.
  19. James Ming Chen, 2021. "An Introduction to Machine Learning for Panel Data," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 27(1), pages 1-16, February.
  20. Teng, Long, 2022. "Gradient boosting-based numerical methods for high-dimensional backward stochastic differential equations," Applied Mathematics and Computation, Elsevier, vol. 426(C).
  21. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
  22. Zheng, Lin & Mueller, Markus & Luo, Chunbo & Yan, Xiaoyu, 2024. "Predicting whole-life carbon emissions for buildings using different machine learning algorithms: A case study on typical residential properties in Cornwall, UK," Applied Energy, Elsevier, vol. 357(C).
  23. Vomfell, Lara & Härdle, Wolfgang Karl & Lessmann, Stefan, 2018. "Improving Crime Count Forecasts Using Twitter and Taxi Data," IRTG 1792 Discussion Papers 2018-013, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  24. Enwei Zhu & Stanislav Sobolevsky, 2018. "House Price Modeling with Digital Census," Papers 1809.03834, arXiv.org.
  25. Joseph Sexton & Petter Laake, 2007. "Boosted Regression Trees with Errors in Variables," Biometrics, The International Biometric Society, vol. 63(2), pages 586-592, June.
  26. Takahiro Yabe & P. Suresh C. Rao & Satish V. Ukkusuri, 2021. "Modeling the Influence of Online Social Media Information on Post-Disaster Mobility Decisions," Sustainability, MDPI, vol. 13(9), pages 1-13, May.
  27. Maria-Carmen García-Centeno & Román Mínguez-Salido & Raúl del Pozo-Rubio, 2021. "The Classification of Profiles of Financial Catastrophe Caused by Out-of-Pocket Payments: A Methodological Approach," Mathematics, MDPI, vol. 9(11), pages 1-20, May.
  28. Jiaming Zeng & Berk Ustun & Cynthia Rudin, 2017. "Interpretable classification models for recidivism prediction," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 689-722, June.
  29. Tesfamariam Engida Mengesha & Lulseged Tamene Desta & Paolo Gamba & Getachew Tesfaye Ayehu, 2024. "Multi-Temporal Passive and Active Remote Sensing for Agricultural Mapping and Acreage Estimation in Context of Small Farm Holds in Ethiopia," Land, MDPI, vol. 13(3), pages 1-29, March.
  30. María Del Carmen Ruiz-Abellón & Antonio Gabaldón & Antonio Guillamón, 2018. "Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees," Energies, MDPI, vol. 11(8), pages 1-22, August.
  31. Sheikh Shah Mohammad Motiur Rahman & Zhihao Chen & Alain Lalande & Thomas Decourselle & Alexandre Cochet & Thibaut Pommier & Yves Cottin & Michel Salomon & Raphaël Couturier, 2023. "Automatic classification of patients with myocardial infarction or myocarditis based only on clinical data: A quick response," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-18, May.
  32. Mohammed Sabri & Rosanna Verde & Antonio Balzanella & Fabrizio Maturo & Hamid Tairi & Ali Yahyaouy & Jamal Riffi, 2024. "A Novel Classification Algorithm Based on the Synergy Between Dynamic Clustering with Adaptive Distances and K-Nearest Neighbors," Journal of Classification, Springer;The Classification Society, vol. 41(2), pages 264-288, July.
  33. Celbiş, Mehmet Güney & Bouzouina, Louafi, 2025. "To what extent walking and biking are substitutes or complements to public transport? Interpretable machine learning findings from the University of Lyon, France," Journal of Transport Geography, Elsevier, vol. 123(C).
  34. Stephen J. Tulowiecki & Brice B. Hanberry & Marc D. Abrams, 2025. "Spatial and Temporal Pervasiveness of Indigenous Settlement in Oak Landscapes of Southern New England, US, During the Late Holocene," Land, MDPI, vol. 14(3), pages 1-25, March.
  35. Mirosław Parol & Paweł Piotrowski & Piotr Kapler & Mariusz Piotrowski, 2021. "Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control," Energies, MDPI, vol. 14(5), pages 1-29, February.
  36. Garre, Alberto & Ruiz, Mari Carmen & Hontoria, Eloy, 2020. "Application of Machine Learning to support production planning of a food industry in the context of waste generation under uncertainty," Operations Research Perspectives, Elsevier, vol. 7(C).
  37. Soleimani, Reza & Abooali, Danial & Shoushtari, Navid Alavi, 2018. "Characterizing CO2 capture with aqueous solutions of LysK and the mixture of MAPA + DEEA using soft computing methods," Energy, Elsevier, vol. 164(C), pages 664-675.
  38. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
  39. Nikolai Stein & Jan Meller & Christoph M. Flath, 2018. "Big data on the shop-floor: sensor-based decision-support for manual processes," Journal of Business Economics, Springer, vol. 88(5), pages 593-616, July.
  40. Reza Rezaee & Jamiu Ekundayo, 2022. "Permeability Prediction Using Machine Learning Methods for the CO 2 Injectivity of the Precipice Sandstone in Surat Basin, Australia," Energies, MDPI, vol. 15(6), pages 1-15, March.
  41. Mariana Hassegawa & Filip Havreljuk & Rock Ouimet & David Auty & David Pothier & Alexis Achim, 2015. "Large-Scale Variations in Lumber Value Recovery of Yellow Birch and Sugar Maple in Quebec, Canada," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-18, August.
  42. Sieun Lee & Eunhae Cho & Geunsoo Jang & Sangil Kim & Giphil Cho, 2022. "Early detection of norovirus outbreak using machine learning methods in South Korea," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-12, November.
  43. Laura Arnal & Pedro Pons-Suñer & J Ramón Navarro-Cerdán & Pablo Ruiz-Valls & Mª Jose Caballero Mateos & Bernardo Valdivieso Martínez & Juan-Carlos Perez-Cortes, 2022. "Decision support through risk cost estimation in 30-day hospital unplanned readmission," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-16, July.
  44. Marwah Soliman & Vyacheslav Lyubchich & Yulia R. Gel, 2020. "Ensemble forecasting of the Zika space‐time spread with topological data analysis," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
  45. Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2020. "Forecasting Realized Stock-Market Volatility: Do Industry Returns have Predictive Value?," Working Papers 2020107, University of Pretoria, Department of Economics.
  46. De Bock, Koen W. & Coussement, Kristof & Lessmann, Stefan, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," European Journal of Operational Research, Elsevier, vol. 285(2), pages 612-630.
  47. Chao Fu & Dongyue Wang & Wenjun Chang, 2023. "Data-driven analysis of influence between radiologists for diagnosis of breast lesions," Annals of Operations Research, Springer, vol. 328(1), pages 419-449, September.
  48. Adam Kiersztyn & Krystyna Kiersztyn & Korneliusz Pylak & Jakub Bis & Michal Dolecki & Anna Zelazna, 2024. "Imputing Data Gaps in Economic Surveys Using Fuzzy Sets and Artificial Intelligence Technique," European Research Studies Journal, European Research Studies Journal, vol. 0(Special B), pages 320-332.
  49. Tsionas, Mike, 2022. "Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries," International Journal of Production Economics, Elsevier, vol. 249(C).
  50. Silvia Figini & Roberto Savona & Marika Vezzoli, 2016. "Corporate Default Prediction Model Averaging: A Normative Linear Pooling Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(1-2), pages 6-20, January.
  51. Ángel Luis Muñoz Castañeda & Noemí DeCastro-García & David Escudero García, 2021. "RHOASo: An Early Stop Hyper-Parameter Optimization Algorithm," Mathematics, MDPI, vol. 9(18), pages 1-52, September.
  52. Hoeschle, Lisa & Wang, Hong Holly & Yu, Xiaohua, 2024. "State-level heterogeneities in US food insecurity – an assessment of long-term predictors," 2024 Annual Meeting, July 28-30, New Orleans, LA 343686, Agricultural and Applied Economics Association.
  53. Oh, Jiyoung & Min, Daiki, 2024. "Prediction of energy consumption for manufacturing small and medium-sized enterprises (SMEs) considering industry characteristics," Energy, Elsevier, vol. 300(C).
  54. Keddouda, Abdelhak & Ihaddadene, Razika & Boukhari, Ali & Atia, Abdelmalek & Arıcı, Müslüm & Lebbihiat, Nacer & Ihaddadene, Nabila, 2024. "Photovoltaic module temperature prediction using various machine learning algorithms: Performance evaluation," Applied Energy, Elsevier, vol. 363(C).
  55. Alcántara Mata, Antonio & Ruiz Mora, Carlos, 2022. "On data-driven chance constraint learning for mixed-integer optimization problems," DES - Working Papers. Statistics and Econometrics. WS 35425, Universidad Carlos III de Madrid. Departamento de Estadística.
  56. Konstantinos Oikonomou & Dimitris Damigos, 2025. "Short term forecasting of base metals prices using a LightGBM and a LightGBM - ARIMA ensemble," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 37-49, March.
  57. Dana Anderson & Rachel A. Davidson & Keisuke Himoto & Charles Scawthorn, 2016. "Statistical Modeling of Fire Occurrence Using Data from the Tōhoku, Japan Earthquake and Tsunami," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 378-395, February.
  58. Kamila Báťková & Svatopluk Matula & Eva Hrúzová & Markéta Miháliková & Recep Serdar Kara & Cansu Almaz, 2022. "A comparison of measured and estimated saturated hydraulic conductivity of various soils in the Czech Republic," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 68(7), pages 338-346.
  59. Wen, Shaoting & Buyukada, Musa & Evrendilek, Fatih & Liu, Jingyong, 2020. "Uncertainty and sensitivity analyses of co-combustion/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models," Renewable Energy, Elsevier, vol. 151(C), pages 463-474.
  60. John M Aiken & Riccardo De Bin & Morten Hjorth-Jensen & Marcos D Caballero, 2020. "Predicting time to graduation at a large enrollment American university," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-28, November.
  61. Akhilesh Prasad & Priti Bakhshi, 2022. "Forecasting the Direction of Daily Changes in the India VIX Index Using Machine Learning," JRFM, MDPI, vol. 15(12), pages 1-26, November.
  62. Klaus Boesch & Flavio A. Ziegelmann, 2025. "Machine Learning Methods and Time Series: A Through Forecasting Study via Simulation and USA Inflation Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 1-34, July.
  63. Doruk Cengiz & Arindrajit Dube & Attila Lindner & David Zentler-Munro, 2022. "Seeing beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes," Journal of Labor Economics, University of Chicago Press, vol. 40(S1), pages 203-247.
  64. Pierdzioch Christian & Gupta Rangan, 2020. "Uncertainty and Forecasts of U.S. Recessions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(4), pages 1-20, September.
  65. Koen W. de Bock & Kristof Coussement & Stefan Lessmann, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," Post-Print hal-02863245, HAL.
  66. Kagie, M. & van Wezel, M.C., 2006. "Hedonic price models and indices based on boosting applied to the Dutch housing market," Econometric Institute Research Papers EI 2006-17, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  67. Akash Malhotra, 2021. "A hybrid econometric–machine learning approach for relative importance analysis: prioritizing food policy," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 549-581, September.
  68. Petra M. Kuhnert & Kerrie Mengersen & Peter Tesar, 2003. "Bridging the Gap between Different Statistical Approaches: An Integrated Framework for Modelling," International Statistical Review, International Statistical Institute, vol. 71(2), pages 335-368, August.
  69. Chen, Yang & Li, Kewen & Zhang, Han & He, Jifu & Bai, Chen & Shen, Quanwei & Lu, Wei, 2025. "Discovering new sweet spots with the geothermal map renovated by machine learning in Henan Province, China," Renewable Energy, Elsevier, vol. 250(C).
  70. Keun Hee Lee & Mali Abdollahian & Sergei Schreider & Sona Taheri, 2023. "Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect," Mathematics, MDPI, vol. 11(11), pages 1-28, May.
  71. Stan Hatko, 2017. "The Bank of Canada 2015 Retailer Survey on the Cost of Payment Methods: Nonresponse," Technical Reports 107, Bank of Canada.
  72. Zemin Gao & Mingtao Ding, 2022. "Application of convolutional neural network fused with machine learning modeling framework for geospatial comparative analysis of landslide susceptibility," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(2), pages 833-858, September.
  73. J. A. Carrillo & M. Nieto & J. F. Velez & D. Velez, 2021. "A New Machine Learning Forecasting Algorithm Based on Bivariate Copula Functions," Forecasting, MDPI, vol. 3(2), pages 1-22, May.
  74. Francisco A. Ramírez-Rivera & Néstor F. Guerrero-Rodríguez, 2024. "Ensemble Learning Algorithms for Solar Radiation Prediction in Santo Domingo: Measurements and Evaluation," Sustainability, MDPI, vol. 16(18), pages 1-27, September.
  75. Regue, Robert & Recker, Will, 2014. "Proactive vehicle routing with inferred demand to solve the bikesharing rebalancing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 72(C), pages 192-209.
  76. Donald Douglas Atsa'am & Ruth Wario, 2021. "Classifier Selection for the Prediction of Dominant Transmission Mode of Coronavirus Within Localities: Predicting COVID-19 Transmission Mode," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global Scientific Publishing, vol. 12(6), pages 1-12, November.
  77. Mehmet Güney Celbiş & Pui‐hang Wong & Karima Kourtit & Peter Nijkamp, 2023. "Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(3), pages 559-584, April.
  78. Angelin Blessy & Avneesh Kumar & Prabagaran A & Abdul Quadir Md & Abdullah I. Alharbi & Ahlam Almusharraf & Surbhi B. Khan, 2023. "Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
  79. Vrontos, Spyridon D. & Galakis, John & Vrontos, Ioannis D., 2021. "Modeling and predicting U.S. recessions using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 37(2), pages 647-671.
  80. Al-Mudhafar, Watheq J. & Rao, Dandina N & Srinivasan, Sanjay, 2019. "Advanced ML and AI Approaches for Proxy-based Optimization of CO2-Enhanced Oil Recovery in Heterogeneous Clastic Reservoirs," Earth Arxiv wsu6g, Center for Open Science.
  81. Konrad Bogner & Florian Pappenberger & Massimiliano Zappa, 2019. "Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts," Sustainability, MDPI, vol. 11(12), pages 1-22, June.
  82. Shan Xu & Chang Ni & Xiangyun Hu, 2023. "Predicting Terrestrial Heat Flow in North China Using Multiple Geological and Geophysical Datasets Based on Machine Learning Method," Energies, MDPI, vol. 16(4), pages 1-14, February.
  83. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
  84. Rokach, Lior, 2009. "Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4046-4072, October.
  85. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
  86. Lu, Jie & Zhang, Chaobo & Li, Junyang & Zhao, Yang & Qiu, Weikang & Li, Tingting & Zhou, Kai & He, Jianing, 2022. "Graph convolutional networks-based method for estimating design loads of complex buildings in the preliminary design stage," Applied Energy, Elsevier, vol. 322(C).
  87. Jing Tan & Lei Lin, 2024. "Exploring key social capital indicators for disaster preparedness in rural disaster-prone areas: a boosted regression tree approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(5), pages 4159-4180, March.
  88. Zhou, Jing & Li, Wei & Wang, Jiaxin & Ding, Shuai & Xia, Chengyi, 2019. "Default prediction in P2P lending from high-dimensional data based on machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
  89. Ding, Xiaosong & Feng, Chong & Yu, Peiling & Li, Kaiwen & Chen, Xi, 2023. "Gradient boosting decision tree in the prediction of NOx emission of waste incineration," Energy, Elsevier, vol. 264(C).
  90. Cleridy E. Lennert‐Cody & Richard A. Berk, 2007. "Statistical learning procedures for monitoring regulatory compliance: an application to fisheries data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 671-689, July.
  91. Christoph Emanuel Mueller, 2016. "Accurate forecast of countries’ research output by macro-level indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 1307-1328, November.
  92. Müller, Daniel & Leitão, Pedro J. & Sikor, Thomas, 2013. "Comparing the determinants of cropland abandonment in Albania and Romania using boosted regression trees," Agricultural Systems, Elsevier, vol. 117(C), pages 66-77.
  93. Zhigao Zhou & Aiwen Lin & Lijie He & Lunche Wang, 2022. "Evaluation of Various Tree-Based Ensemble Models for Estimating Solar Energy Resource Potential in Different Climatic Zones of China," Energies, MDPI, vol. 15(9), pages 1-23, May.
  94. Nathaniel MacNell & Lydia Feinstein & Jesse Wilkerson & Pӓivi M Salo & Samantha A Molsberry & Michael B Fessler & Peter S Thorne & Alison A Motsinger-Reif & Darryl C Zeldin, 2023. "Implementing machine learning methods with complex survey data: Lessons learned on the impacts of accounting sampling weights in gradient boosting," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-15, January.
  95. Tyler Pike & Horacio Sapriza & Tom Zimmermann, 2019. "Bottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults using Machine Learning," Finance and Economics Discussion Series 2019-070, Board of Governors of the Federal Reserve System (U.S.).
  96. Kaijie Xu & Xiaochen Zhang & Lin Qiu, 2025. "Explainable Warm-Start Point Learning for AC Optimal Power Flow Using a Novel Hybrid Stacked Ensemble Method," Sustainability, MDPI, vol. 17(2), pages 1-18, January.
  97. Tao, Tao & Cao, Jason, 2024. "Ineffective built environment interventions: How to reduce driving in American suburbs?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
  98. Guo, Shuhui & Chen, Huan & Dai, Bitao & Wang, Mengning & Liu, Shuo & Zhang, Cheng & Liu, Shan & Lu, Xin, 2025. "Influential factors of streamer popularity in the live streaming platform," Journal of Retailing and Consumer Services, Elsevier, vol. 84(C).
  99. Christian Troost & Julia Parussis-Krech & Matías Mejaíl & Thomas Berger, 2023. "Boosting the Scalability of Farm-Level Models: Efficient Surrogate Modeling of Compositional Simulation Output," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 721-759, October.
  100. Lauri Nevasalmi, 2022. "Recession forecasting with high‐dimensional data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 752-764, July.
  101. Łukasz Wojtecki & Sebastian Iwaszenko & Derek B. Apel & Tomasz Cichy, 2021. "An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine," Energies, MDPI, vol. 14(21), pages 1-18, October.
  102. Linwei Hu & Jie Chen & Joel Vaughan & Soroush Aramideh & Hanyu Yang & Kelly Wang & Agus Sudjianto & Vijayan N. Nair, 2021. "Supervised Machine Learning Techniques: An Overview with Applications to Banking," International Statistical Review, International Statistical Institute, vol. 89(3), pages 573-604, December.
  103. Giandomenico Domenico & Annamaria Tuan & Marco Visentin, 2021. "Linguistic drivers of misinformation diffusion on social media during the COVID-19 pandemic," Italian Journal of Marketing, Springer, vol. 2021(4), pages 351-369, December.
  104. Ascher, Simon & Watson, Ian & You, Siming, 2022. "Machine learning methods for modelling the gasification and pyrolysis of biomass and waste," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
  105. Gao, Zitian & Guo, Danlu & Ryu, Dongryeol & Western, Andrew W., 2025. "Benchmarking farm-level cotton water productivity using on-farm irrigation measurements and remotely sensed yields," Agricultural Water Management, Elsevier, vol. 311(C).
  106. Musaab I. Magzoub & Raj Kiran & Saeed Salehi & Ibnelwaleed A. Hussein & Mustafa S. Nasser, 2021. "Assessing the Relation between Mud Components and Rheology for Loss Circulation Prevention Using Polymeric Gels: A Machine Learning Approach," Energies, MDPI, vol. 14(5), pages 1-19, March.
  107. Pedro, Hugo T.C. & Coimbra, Carlos F.M. & David, Mathieu & Lauret, Philippe, 2018. "Assessment of machine learning techniques for deterministic and probabilistic intra-hour solar forecasts," Renewable Energy, Elsevier, vol. 123(C), pages 191-203.
  108. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
  109. Vladimir Mironovich Vishnevsky & Valentina Ivanovna Klimenok & Aleksandr Mikhailovich Sokolov & Andrey Alekseevich Larionov, 2024. "Investigation of the Fork–Join System with Markovian Arrival Process Arrivals and Phase-Type Service Time Distribution Using Machine Learning Methods," Mathematics, MDPI, vol. 12(5), pages 1-22, February.
  110. Peiró-Signes, Ángel & Segarra-Oña, Marival & Trull-Domínguez, Óscar & Sánchez-Planelles, Joaquín, 2022. "Exposing the ideal combination of endogenous–exogenous drivers for companies’ ecoinnovative orientation: Results from machine-learning methods," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
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