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Stochastic gradient boosting

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Cited by:

  1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
  2. 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.
  3. 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.
  4. 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.
  5. Luca Badolato & Ari Gabriel Decter-Frain & Nicolas Irons & Maria Laura 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.
  6. Delen, Dursun & Zolbanin, Hamed M., 2018. "The analytics paradigm in business research," Journal of Business Research, Elsevier, vol. 90(C), pages 186-195.
  7. 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.
  8. 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.
  9. Teng, Long, 2022. "Gradient boosting-based numerical methods for high-dimensional backward stochastic differential equations," Applied Mathematics and Computation, Elsevier, vol. 426(C).
  10. Enwei Zhu & Stanislav Sobolevsky, 2018. "House Price Modeling with Digital Census," Papers 1809.03834, arXiv.org.
  11. Joseph Sexton & Petter Laake, 2007. "Boosted Regression Trees with Errors in Variables," Biometrics, The International Biometric Society, vol. 63(2), pages 586-592, June.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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).
  17. 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.
  18. 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).
  19. 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.
  20. 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.
  21. 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.
  22. Ba Chu & Shafiullah Qureshi, 2023. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1567-1609, December.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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).
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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, vol. 12(6), pages 1-12, November.
  35. 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.
  36. 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.
  37. 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.
  38. 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.
  39. 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).
  40. 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).
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. 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).
  49. Guelman, Leo & Guillén, Montserrat & Pérez-Marín, Ana M., 2014. "A survey of personalized treatment models for pricing strategies in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 58(C), pages 68-76.
  50. Adler, Werner & Lausen, Berthold, 2009. "Bootstrap estimated true and false positive rates and ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 718-729, January.
  51. Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2024. "Big data financial transactions and GDP nowcasting: The case of Turkey," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 227-248, March.
  52. Ricardo Aler & Javier Huertas-Tato & José M. Valls & Inés M. Galván, 2019. "Improving Prediction Intervals Using Measured Solar Power with a Multi-Objective Approach," Energies, MDPI, vol. 12(24), pages 1-19, December.
  53. Philippe Goulet Coulombe, 2020. "To Bag is to Prune," Papers 2008.07063, arXiv.org, revised Sep 2024.
    • Philippe Goulet Coulombe, 2021. "To Bag is to Prune," Working Papers 21-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Jun 2021.
  54. L. Lombardo & M. Cama & C. Conoscenti & M. Märker & E. Rotigliano, 2015. "Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messi," 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. 79(3), pages 1621-1648, December.
  55. Aurélie Lemmens & Sunil Gupta, 2020. "Managing Churn to Maximize Profits," Marketing Science, INFORMS, vol. 39(5), pages 956-973, September.
  56. Mehdi Jamei & Mumtaz Ali & Anurag Malik & Ramendra Prasad & Shahab Abdulla & Zaher Mundher Yaseen, 2022. "Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time-Varying Filtered Empirical Mode Decomposition Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4637-4676, September.
  57. Manish Pandey & Masoud Karbasi & Mehdi Jamei & Anurag Malik & Jaan H. Pu, 2023. "A Comprehensive Experimental and Computational Investigation on Estimation of Scour Depth at Bridge Abutment: Emerging Ensemble Intelligent Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3745-3767, July.
  58. Ioannis Nasios & Konstantinos Vogklis, 2023. "Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series," Papers 2310.13029, arXiv.org.
  59. Divya Chandran & N. R. Chithra, 2025. "Predictive Performance of Ensemble Learning Boosting Techniques in Daily Streamflow Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(3), pages 1235-1259, February.
  60. Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
  61. Richard Berk, 2019. "Accuracy and Fairness for Juvenile Justice Risk Assessments," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 16(1), pages 175-194, March.
  62. Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Working Papers 21-02, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
  63. Jo~ao B. Assunc{c}~ao & Pedro Afonso Fernandes, 2024. "The Surprising Robustness of Partial Least Squares," Papers 2409.05713, arXiv.org.
  64. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
  65. Simon J Pittman & Kerry A Brown, 2011. "Multi-Scale Approach for Predicting Fish Species Distributions across Coral Reef Seascapes," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-12, May.
  66. Teramoto Reiji, 2009. "Balanced Gradient Boosting from Imbalanced Data for Clinical Outcome Prediction," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-21, April.
  67. Huang, Chengwei & Xu, Jialing & Xu, Shuai & Shan, Murong & Liu, Shanke & Yu, Lijun, 2024. "Optimizing H2 production from biomass: A machine learning-enhanced model of supercritical water gasification dynamics," Energy, Elsevier, vol. 312(C).
  68. Laura Alessandretti & Abeer ElBahrawy & Luca Maria Aiello & Andrea Baronchelli, 2018. "Anticipating cryptocurrency prices using machine learning," Papers 1805.08550, arXiv.org, revised Nov 2018.
  69. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
  70. Alexandre Momparler & Pedro Carmona & Francisco Climent, 2025. "Catalyzing Sustainable Investment: Revealing ESG Power in Predicting Fund Performance with Machine Learning," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1617-1642, March.
  71. Zhang, Wenwen & Robinson, Caleb & Guhathakurta, Subhrajit & Garikapati, Venu M. & Dilkina, Bistra & Brown, Marilyn A. & Pendyala, Ram M., 2018. "Estimating residential energy consumption in metropolitan areas: A microsimulation approach," Energy, Elsevier, vol. 155(C), pages 162-173.
  72. Junming Liu & Mingfei Teng & Weiwei Chen & Hui Xiong, 2023. "A Cost-Effective Sequential Route Recommender System for Taxi Drivers," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1098-1119, September.
  73. Marta Serra-Garcia & Uri Gneezy, 2023. "Improving Human Deception Detection Using Algorithmic Feedback," CESifo Working Paper Series 10518, CESifo.
  74. Hui Hu & Jianfeng Zhang & Tao Li, 2021. "A Novel Hybrid Decompose-Ensemble Strategy with a VMD-BPNN Approach for Daily Streamflow Estimating," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5119-5138, December.
  75. Louise Columelli & Miguel Núñez del Prado & Leoncio Zarate-Gamarra, 2016. "Measuring Churner Influence on Pre-paid Subscribers Using Fuzzy Logic," Working Papers 16-22, Centro de Investigación, Universidad del Pacífico.
  76. Oskar Maier & Christoph Schröder & Nils Daniel Forkert & Thomas Martinetz & Heinz Handels, 2015. "Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-16, December.
  77. Saifur Rahman & Muhammad Irfan & Mohsin Raza & Khawaja Moyeezullah Ghori & Shumayla Yaqoob & Muhammad Awais, 2020. "Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living," IJERPH, MDPI, vol. 17(3), pages 1-15, February.
  78. Joyce de Souza Zanirato Maia & Ana Paula Arantes Bueno & João Ricardo Sato, 2021. "Assessing the educational performance of different Brazilian school cycles using data science methods," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-14, March.
  79. Frenger, Monika & Emrich, Eike & Geber, Sebastian & Follert, Florian & Pierdzioch, Christian, 2019. "The influence of performance parameters on market value," Working Papers of the European Institute for Socioeconomics 30, European Institute for Socioeconomics (EIS), Saarbrücken.
  80. Julia Wamsler & Denis Vuckovac & Martin Natter & Alexander Ilic, 2024. "Live shopping promotions: which categories should a retailer discount to shoppers already in the store?," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(1), pages 135-174, March.
  81. Mehmet Güney Celbiş & Pui-Hang Wong & Karima Kourtit & Peter Nijkamp, 2023. "Job Satisfaction and the ‘Great Resignation’: An Exploratory Machine Learning Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 170(3), pages 1097-1118, December.
  82. Laviolette, Jérôme & Morency, Catherine & Waygood, E.O.D., 2022. "A kilometer or a mile? Does buffer size matter when it comes to car ownership?," Journal of Transport Geography, Elsevier, vol. 104(C).
  83. Cáceres, Neila & Malone, Samuel W., 2013. "Forecasting leadership transitions around the world," International Journal of Forecasting, Elsevier, vol. 29(4), pages 575-591.
  84. Eike Emrich & Christian Pierdzioch, 2016. "Volunteering, Match Quality, and Internet Use," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 136(2), pages 199-226.
  85. Cong Wang & Kai Zhan & Xigui Zheng & Cancan Liu & Chao Kong, 2024. "A Method for Evaluating the Data Integrity of Microseismic Monitoring Systems in Mines Based on a Gradient Boosting Algorithm," Mathematics, MDPI, vol. 12(12), pages 1-19, June.
  86. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
  87. Marco Quartulli & Amaia Gil & Ane Miren Florez-Tapia & Pablo Cereijo & Elixabete Ayerbe & Igor G. Olaizola, 2021. "Ensemble Surrogate Models for Fast LIB Performance Predictions," Energies, MDPI, vol. 14(14), pages 1-17, July.
  88. Neal Hughes & Michael Lu & Wei Ying Soh & Kenton Lawson, 2022. "Modelling the effects of climate change on the profitability of Australian farms," Climatic Change, Springer, vol. 172(1), pages 1-22, May.
  89. Mengting Yao & Yun Zhu & Junjie Li & Hua Wei & Penghui He, 2019. "Research on Predicting Line Loss Rate in Low Voltage Distribution Network Based on Gradient Boosting Decision Tree," Energies, MDPI, vol. 12(13), pages 1-14, June.
  90. Oz, Ibrahim Onur & Yelkenci, Tezer & Meral, Gorkem, 2021. "The role of earnings components and machine learning on the revelation of deteriorating firm performance," International Review of Financial Analysis, Elsevier, vol. 77(C).
  91. Díaz-Yáñez, Olalla & Mola-Yudego, Blas & González-Olabarria, José Ramón, 2019. "Modelling damage occurrence by snow and wind in forest ecosystems," Ecological Modelling, Elsevier, vol. 408(C), pages 1-1.
  92. Nasios, Ioannis & Vogklis, Konstantinos, 2022. "Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1448-1459.
  93. Robert Suchting & Michael S. Businelle & Stephen W. Hwang & Nikhil S. Padhye & Yijiong Yang & Diane M. Santa Maria, 2020. "Predicting Daily Sheltering Arrangements among Youth Experiencing Homelessness Using Diary Measurements Collected by Ecological Momentary Assessment," IJERPH, MDPI, vol. 17(18), pages 1-17, September.
  94. Scott Wentland & Gary Cornwall & Jeremy G. Moulton, 2023. "For What It's Worth: Measuring Land Value in the Era of Big Data and Machine Learning," BEA Papers 0115, Bureau of Economic Analysis.
  95. Thebelt, Alexander & Tsay, Calvin & Lee, Robert M. & Sudermann-Merx, Nathan & Walz, David & Tranter, Tom & Misener, Ruth, 2022. "Multi-objective constrained optimization for energy applications via tree ensembles," Applied Energy, Elsevier, vol. 306(PB).
  96. Matthias Bogaert & Michel Ballings & Martijn Hosten & Dirk Van den Poel, 2017. "Identifying Soccer Players on Facebook Through Predictive Analytics," Decision Analysis, INFORMS, vol. 14(4), pages 274-297, December.
  97. Eline Auwera & Bert D’Espallier & Roy Mersland, 2024. "Achieving Double Bottom-Line Performance in Hybrid Organisations: A Machine-Learning Approach," Journal of Business Ethics, Springer, vol. 190(3), pages 625-647, March.
  98. Barış Soybilgen & Ege Yazgan, 2021. "Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 387-417, January.
  99. Kadziński, Miłosz & Ghaderi, Mohammad & Dąbrowski, Maciej, 2020. "Contingent preference disaggregation model for multiple criteria sorting problem," European Journal of Operational Research, Elsevier, vol. 281(2), pages 369-387.
  100. Daniel Yoo & Gillian Divard & Marc Raynaud & Aaron Cohen & Tom D. Mone & John Thomas Rosenthal & Andrew J. Bentall & Mark D. Stegall & Maarten Naesens & Huanxi Zhang & Changxi Wang & Juliette Gueguen , 2024. "A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  101. Manuel Zamudio López & Hamidreza Zareipour & Mike Quashie, 2024. "Forecasting the Occurrence of Electricity Price Spikes: A Statistical-Economic Investigation Study," Forecasting, MDPI, vol. 6(1), pages 1-23, February.
  102. Tsao, Yu-Chung & Chen, Yu-Kai & Chiu, Shih-Hao & Lu, Jye-Chyi & Vu, Thuy-Linh, 2022. "An innovative demand forecasting approach for the server industry," Technovation, Elsevier, vol. 110(C).
  103. Emrich Eike & Pierdzioch Christian, 2016. "Public Goods, Private Consumption, and Human Capital: Using Boosted Regression Trees to Model Volunteer Labour Supply," Review of Economics, De Gruyter, vol. 67(3), pages 263-283, December.
  104. Linwei Hu & Jie Chen & Joel Vaughan & Hanyu Yang & Kelly Wang & Agus Sudjianto & Vijayan N. Nair, 2020. "Supervised Machine Learning Techniques: An Overview with Applications to Banking," Papers 2008.04059, arXiv.org.
  105. Adrian Carballal & Carlos Fernandez-Lozano & Nereida Rodriguez-Fernandez & Luz Castro & Antonino Santos, 2019. "Avoiding the Inherent Limitations in Datasets Used for Measuring Aesthetics When Using a Machine Learning Approach," Complexity, Hindawi, vol. 2019, pages 1-12, January.
  106. Phathutshedzo Mpfumali & Caston Sigauke & Alphonce Bere & Sophie Mulaudzi, 2019. "Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data," Energies, MDPI, vol. 12(18), pages 1-28, September.
  107. Seyedzadeh, Saleh & Pour Rahimian, Farzad & Oliver, Stephen & Rodriguez, Sergio & Glesk, Ivan, 2020. "Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making," Applied Energy, Elsevier, vol. 279(C).
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