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Stochastic gradient boosting

Citations

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Cited by:

  1. 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.
  2. 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.
  3. Teng, Long, 2022. "Gradient boosting-based numerical methods for high-dimensional backward stochastic differential equations," Applied Mathematics and Computation, Elsevier, vol. 426(C).
  4. 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.
  5. 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.
  6. 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.
  7. 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).
  8. 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.
  9. 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.
  10. 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.
  11. 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).
  12. 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.
  13. 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.
  14. Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Papers 2103.01926, arXiv.org, revised Jul 2021.
  15. 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.
  16. Laura Alessandretti & Abeer ElBahrawy & Luca Maria Aiello & Andrea Baronchelli, 2018. "Anticipating cryptocurrency prices using machine learning," Papers 1805.08550, arXiv.org, revised Nov 2018.
  17. 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.
  18. 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.
  19. 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).
  20. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
  21. 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.
  22. 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).
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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).
  29. Huei-Wen Teng & Yu-Hsien Li, 2023. "Can deep neural networks outperform Fama-MacBeth regression and other supervised learning approaches in stock returns prediction with asset-pricing factors?," Digital Finance, Springer, vol. 5(1), pages 149-182, March.
  30. Lu, Yingjie & Li, Tao & Hu, Hui & Zeng, Xuemei, 2023. "Short-term prediction of reference crop evapotranspiration based on machine learning with different decomposition methods in arid areas of China," Agricultural Water Management, Elsevier, vol. 279(C).
  31. Sun-Youn Shin & Han-Gyun Woo, 2022. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.
  32. Sabyasachi Mohapatra & Rohan Mukherjee & Arindam Roy & Anirban Sengupta & Amit Puniyani, 2022. "Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators?," JRFM, MDPI, vol. 15(8), pages 1-16, August.
  33. David, Mathieu & Luis, Mazorra Aguiar & Lauret, Philippe, 2018. "Comparison of intraday probabilistic forecasting of solar irradiance using only endogenous data," International Journal of Forecasting, Elsevier, vol. 34(3), pages 529-547.
  34. 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.
  35. Burim Ramosaj & Markus Pauly, 2019. "Predicting missing values: a comparative study on non-parametric approaches for imputation," Computational Statistics, Springer, vol. 34(4), pages 1741-1764, December.
  36. Shang, Gang & Xu, Liyun & Tian, Jinzhu & Cai, Dongwei & Xu, Zhun & Zhou, Zhuo, 2023. "A real-time green construction optimization strategy for engineering vessels considering fuel consumption and productivity: A case study on a cutter suction dredger," Energy, Elsevier, vol. 274(C).
  37. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
  38. Evangelos Spiliotis & Fotios Petropoulos & Konstantinos Nikolopoulos, 2020. "The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece," Energies, MDPI, vol. 13(8), pages 1-18, April.
  39. Chun-Chih Chen & Yu-Shing Chang, 2024. "Leveraging Circular Economy Metrics for Data-Driven Forecasting of Solid Waste Production in Europe," Sustainability, MDPI, vol. 16(3), pages 1-15, January.
  40. Distaso, Walter & Roccazzella, Francesco & Vrins, Frédéric, 2023. "Business cycle and realized losses in the consumer credit industry," LIDAM Discussion Papers LFIN 2023007, Université catholique de Louvain, Louvain Finance (LFIN).
  41. Olkis, Christopher & Brandani, Stefano & Santori, Giulio, 2019. "Design and experimental study of a small scale adsorption desalinator," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  42. 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.
  43. Jiaming Zeng & Michael F. Gensheimer & Daniel L. Rubin & Susan Athey & Ross D. Shachter, 2022. "Uncovering interpretable potential confounders in electronic medical records," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
  44. Qun Sui & Sujit K. Ghosh, 2024. "Active Learning for Stacking and AdaBoost-Related Models," Stats, MDPI, vol. 7(1), pages 1-28, January.
  45. Vaughan, Gregory, 2020. "Efficient big data model selection with applications to fraud detection," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1116-1127.
  46. 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.
  47. Bohdan M. Pavlyshenko, 2019. "Machine-Learning Models for Sales Time Series Forecasting," Data, MDPI, vol. 4(1), pages 1-11, January.
  48. 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.
  49. 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".
  50. 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.
  51. 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).
  52. 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.
  53. 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.
  54. 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.
  55. 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).
  56. Kusiak, Andrew & Li, Wenyan & Song, Zhe, 2010. "Dynamic control of wind turbines," Renewable Energy, Elsevier, vol. 35(2), pages 456-463.
  57. Vitor Azevedo & Christopher Hoegner, 2023. "Enhancing stock market anomalies with machine learning," Review of Quantitative Finance and Accounting, Springer, vol. 60(1), pages 195-230, January.
  58. Jingxuan Zhao & Weiyu Yu & Kun He & Kun Zhao & Chunliang Zhou & Jim A. Wright & Fayun Li, 2023. "Evaluating the Urban-Rural Differences in the Environmental Factors Affecting Amphibian Roadkill," Sustainability, MDPI, vol. 15(7), pages 1-16, March.
  59. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
  60. Shinitzky, Hilla & Shemesh, Yhonatan & Leiser, David & Gilead, Michael, 2024. "Improving geopolitical forecasts with 100 brains and one computer," International Journal of Forecasting, Elsevier, vol. 40(3), pages 958-970.
  61. Chuan Ding & Donggen Wang & Xiaolei Ma & Haiying Li, 2016. "Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees," Sustainability, MDPI, vol. 8(11), pages 1-16, October.
  62. Paulino José Garcia Nieto & Esperanza García Gonzalo & Fernando Sanchez Lasheras & Antonio Bernardo Sánchez, 2020. "A Hybrid Predictive Approach for Chromium Layer Thickness in the Hard Chromium Plating Process Based on the Differential Evolution/Gradient Boosted Regression Tree Methodology," Mathematics, MDPI, vol. 8(6), pages 1-20, June.
  63. Vladimir Vishnevsky & Valentina Klimenok & Alexander Sokolov & Andrey Larionov, 2021. "Performance Evaluation of the Priority Multi-Server System MMAP/PH/M/N Using Machine Learning Methods," Mathematics, MDPI, vol. 9(24), pages 1-27, December.
  64. Paolo Massaro & Ilaria Vannini & Oliver Giudice, 2020. "Institutional sector classifier, a machine learning approach," Questioni di Economia e Finanza (Occasional Papers) 548, Bank of Italy, Economic Research and International Relations Area.
  65. Ivanna Baturynska & Kristian Martinsen, 2021. "Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 179-200, January.
  66. Banafshe Parizad & Hassan Ranjbarzadeh & Ali Jamali & Hamid Khayyam, 2024. "An Intelligent Hybrid Machine Learning Model for Sustainable Forecasting of Home Energy Demand and Electricity Price," Sustainability, MDPI, vol. 16(6), pages 1-17, March.
  67. Yunjong Kim & Juneyoung Park & Cheol Oh, 2021. "A Crash Prediction Method Based on Artificial Intelligence Techniques and Driving Behavior Event Data," Sustainability, MDPI, vol. 13(11), pages 1-15, May.
  68. Quan Zhiyu & Valdez Emiliano A., 2018. "Predictive analytics of insurance claims using multivariate decision trees," Dependence Modeling, De Gruyter, vol. 6(1), pages 377-407, December.
  69. Huang Lin & Merete Eggesbø & Shyamal Das Peddada, 2022. "Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
  70. Carmona, Pedro & Dwekat, Aladdin & Mardawi, Zeena, 2022. "No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure," Research in International Business and Finance, Elsevier, vol. 61(C).
  71. Alexandre Lucas & Ricardo Barranco & Nazir Refa, 2019. "EV Idle Time Estimation on Charging Infrastructure, Comparing Supervised Machine Learning Regressions," Energies, MDPI, vol. 12(2), pages 1-17, January.
  72. Lee, Yongsung & Lee, Bumsoo, 2022. "What’s eating public transit in the United States? Reasons for declining transit ridership in the 2010s," Transportation Research Part A: Policy and Practice, Elsevier, vol. 157(C), pages 126-143.
  73. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
  74. Cai, Jianchao & Xu, Kai & Zhu, Yanhui & Hu, Fang & Li, Liuhuan, 2020. "Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest," Applied Energy, Elsevier, vol. 262(C).
  75. Somodi, Imelda & Bede-Fazekas, Ákos & Botta-Dukát, Zoltán & Molnár, Zsolt, 2024. "Confidence and consistency in discrimination: A new family of evaluation metrics for potential distribution models," Ecological Modelling, Elsevier, vol. 491(C).
  76. Igor Shuryak, 2017. "Advantages of Synthetic Noise and Machine Learning for Analyzing Radioecological Data Sets," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-19, January.
  77. Jackson, Ilya & Ivanov, Dmitry, 2023. "A beautiful shock? Exploring the impact of pandemic shocks on the accuracy of AI forecasting in the beauty care industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
  78. Thitiphat Klinsuwan & Wachiraphong Ratiphaphongthon & Rabian Wangkeeree & Rattanaporn Wangkeeree & Chatchai Sirisamphanwong, 2023. "Evaluation of Machine Learning Algorithms for Supervised Anomaly Detection and Comparison between Static and Dynamic Thresholds in Photovoltaic Systems," Energies, MDPI, vol. 16(4), pages 1-22, February.
  79. 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.
  80. Leng, Erwei & He, Ben & Chen, Jingwei & Liao, Gaoliang & Ma, Yinjie & Zhang, Feng & Liu, Shuai & E, Jiaqiang, 2021. "Prediction of three-phase product distribution and bio-oil heating value of biomass fast pyrolysis based on machine learning," Energy, Elsevier, vol. 236(C).
  81. María Jesús Segovia‐Vargas & I. Marta Miranda‐García & Freddy Alejandro Oquendo‐Torres, 2023. "Sustainable finance: The role of savings and credit cooperatives in Ecuador," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 94(3), pages 951-980, September.
  82. Teresa Gonçalves & Rute Veladas & Hua Yang & Renata Vieira & Paulo Quaresma & Paulo Infante & Cátia Sousa Pinto & João Oliveira & Maria Cortes Ferreira & Jéssica Morais & Ana Raquel Pereira & Nuno Fer, 2023. "Clinical Screening Prediction in the Portuguese National Health Service: Data Analysis, Machine Learning Models, Explainability and Meta-Evaluation," Future Internet, MDPI, vol. 15(1), pages 1-25, January.
  83. Nagy, Gábor I. & Barta, Gergő & Kazi, Sándor & Borbély, Gyula & Simon, Gábor, 2016. "GEFCom2014: Probabilistic solar and wind power forecasting using a generalized additive tree ensemble approach," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1087-1093.
  84. Jamei, Mehdi & Karbasi, Masoud & Malik, Anurag & Jamei, Mozhdeh & Kisi, Ozgur & Yaseen, Zaher Mundher, 2022. "Long-term multi-step ahead forecasting of root zone soil moisture in different climates: Novel ensemble-based complementary data-intelligent paradigms," Agricultural Water Management, Elsevier, vol. 269(C).
  85. John Coglianese & Brendan M. Price, 2020. "Income in the Off-Season: Household Adaptation to Yearly Work Interruptions," Upjohn Working Papers 20-337, W.E. Upjohn Institute for Employment Research.
  86. 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.
  87. 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.
  88. 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.
  89. 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.
  90. 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.
  91. 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.
  92. 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.
  93. 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.
  94. 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.
  95. 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.
  96. 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.
  97. 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.
  98. 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.
  99. 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.
  100. Marta Serra-Garcia & Uri Gneezy, 2023. "Improving Human Deception Detection Using Algorithmic Feedback," CESifo Working Paper Series 10518, CESifo.
  101. 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.
  102. 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.
  103. 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).
  104. Fischer, Thomas & Krauss, Christopher & Treichel, Alex, 2018. "Machine learning for time series forecasting - a simulation study," FAU Discussion Papers in Economics 02/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
  105. Sharan Srinivas, 2020. "A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers," IJERPH, MDPI, vol. 17(10), pages 1-15, May.
  106. Mansoor Ahmed & Anirudh Sriram & Sanjay Singh, 2020. "Short Term Firm-Specific Stock Forecasting with BDI Framework," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 745-778, March.
  107. Niaz Muhammad Shahani & Xigui Zheng & Xiaowei Guo & Xin Wei, 2022. "Machine Learning-Based Intelligent Prediction of Elastic Modulus of Rocks at Thar Coalfield," Sustainability, MDPI, vol. 14(6), pages 1-24, March.
  108. Santos Henrique Brant Dias & Roberto Filgueiras & Elpídio Inácio Fernandes Filho & Gemima Santos Arcanjo & Gustavo Henrique da Silva & Everardo Chartuni Mantovani & Fernando França da Cunha, 2021. "Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-19, February.
  109. Simon Sosvilla-Rivero & Pedro Rodriguez, 2010. "Linkages in international stock markets: evidence from a classification procedure," Applied Economics, Taylor & Francis Journals, vol. 42(16), pages 2081-2089.
  110. repec:bea:wpaper:0209 is not listed on IDEAS
  111. Daan Kolkman & Arjen van Witteloostuijn, 2019. "Data Science in Strategy: Machine learning and text analysis in the study of firm growth," Tinbergen Institute Discussion Papers 19-066/VI, Tinbergen Institute.
  112. Fang, Ming & Taylor, Stephen, 2021. "A machine learning based asset pricing factor model comparison on anomaly portfolios," Economics Letters, Elsevier, vol. 204(C).
  113. Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2021. "Big Data Information and Nowcasting: Consumption and Investment from Bank Transactions in Turkey," Papers 2107.03299, arXiv.org.
  114. Zhang, Hong & Nguyen, Hoang & Vu, Diep-Anh & Bui, Xuan-Nam & Pradhan, Biswajeet, 2021. "Forecasting monthly copper price: A comparative study of various machine learning-based methods," Resources Policy, Elsevier, vol. 73(C).
  115. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
  116. Nahushananda Chakravarthy H G & Karthik M Seenappa & Sujay Raghavendra Naganna & Dayananda Pruthviraja, 2023. "Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
  117. Marlene A. Smith & Murray J. Côté, 2022. "Predictive Analytics Improves Sales Forecasts for a Pop-up Retailer," Interfaces, INFORMS, vol. 52(4), pages 379-389, July.
  118. Sinan Deng & John Inekwe & Vladimir Smirnov & Andrew Wait & Chao Wang, 2023. "Machine Learning and Deep Learning Forecasts of Electricity Imbalance Prices," Working Papers 2023-03, University of Sydney, School of Economics.
  119. Tim Voigt & Martin Kohlhase & Oliver Nelles, 2021. "Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge," Mathematics, MDPI, vol. 9(19), pages 1-26, October.
  120. Sunmin Lee & Yunjung Hyun & Moung-Jin Lee, 2019. "Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
  121. Albrecht, Tobias & Rausch, Theresa Maria & Derra, Nicholas Daniel, 2021. "Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting," Journal of Business Research, Elsevier, vol. 123(C), pages 267-278.
  122. Anton Kocheturov & Panos M. Pardalos & Athanasia Karakitsiou, 2019. "Massive datasets and machine learning for computational biomedicine: trends and challenges," Annals of Operations Research, Springer, vol. 276(1), pages 5-34, May.
  123. Pham, Xuan T.T. & Ho, Tin H., 2021. "Using boosting algorithms to predict bank failure: An untold story," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 40-54.
  124. Martinez, Waldyn & Gray, J. Brian, 2016. "Noise peeling methods to improve boosting algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 483-497.
  125. Puneet Agarwal & Junlin Tang & Adithya Narayanan Lakshmi Narayanan & Jun Zhuang, 2020. "Big Data and Predictive Analytics in Fire Risk Using Weather Data," Risk Analysis, John Wiley & Sons, vol. 40(7), pages 1438-1449, July.
  126. repec:jss:jstsof:17:i02 is not listed on IDEAS
  127. 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.
  128. 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.
  129. 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.
  130. 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).
  131. repec:cte:wsrepe:35425 is not listed on IDEAS
  132. 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.
  133. 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.
  134. 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.
  135. 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.
  136. 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.
  137. 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.
  138. 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.
  139. 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.
  140. Lauri Nevasalmi, 2022. "Recession forecasting with high‐dimensional data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 752-764, July.
  141. 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.
  142. Aditya Chakraborty & Chris P. Tsokos, 2021. "A Real Data-Driven Clustering Approach for Countries Based on Happiness Score," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(Special15), pages 1031-1031, November.
  143. Bharadwaj Kadiyala & Özalp Özer & A. Serdar Şimşek, 2021. "Data‐Driven Approaches to Targeting Promotion E‐mails: The Case of Delayed Incentives," Production and Operations Management, Production and Operations Management Society, vol. 30(3), pages 766-782, March.
  144. Justyna Tomala & Mateusz Mierzejewski & Maria Urbaniec & Sergio Martinez, 2021. "Towards Sustainable Energy Development in Sub-Saharan Africa: Challenges and Opportunities," Energies, MDPI, vol. 14(19), pages 1-18, September.
  145. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
  146. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
  147. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
  148. Smyl, Slawek & Hua, N. Grace, 2019. "Machine learning methods for GEFCom2017 probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1424-1431.
  149. Islam, Towhidul & Meade, Nigel & Carson, Richard T. & Louviere, Jordan J. & Wang, Juan, 2022. "The usefulness of socio-demographic variables in predicting purchase decisions: Evidence from machine learning procedures," Journal of Business Research, Elsevier, vol. 151(C), pages 324-338.
  150. Qinghe Zhao & Zifang Zhang & Yuchen Huang & Junlong Fang, 2022. "TPE-RBF-SVM Model for Soybean Categories Recognition in Selected Hyperspectral Bands Based on Extreme Gradient Boosting Feature Importance Values," Agriculture, MDPI, vol. 12(9), pages 1-16, September.
  151. Diego Opazo & Sebastián Moreno & Eduardo Álvarez-Miranda & Jordi Pereira, 2021. "Analysis of First-Year University Student Dropout through Machine Learning Models: A Comparison between Universities," Mathematics, MDPI, vol. 9(20), pages 1-27, October.
  152. Ollech, Daniel & Webel, Karsten, 2020. "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers 55/2020, Deutsche Bundesbank.
  153. Giacomo De Giorgi & Matthew Harding & Gabriel Vasconcelos, 2021. "Predicting Mortality from Credit Reports," Papers 2111.03662, arXiv.org.
  154. Maryna Krylova & Yarema Okhrin, 2022. "Managing air quality: Predicting exceedances of legal limits for PM10 and O3 concentration using machine learning methods," Environmetrics, John Wiley & Sons, Ltd., vol. 33(2), March.
  155. Fuat Kaya & Gaurav Mishra & Rosa Francaviglia & Ali Keshavarzi, 2023. "Combining Digital Covariates and Machine Learning Models to Predict the Spatial Variation of Soil Cation Exchange Capacity," Land, MDPI, vol. 12(4), pages 1-20, April.
  156. Kim, Jongwoo & Kim, Hongil & Geum, Youngjung, 2023. "How to succeed in the market? Predicting startup success using a machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
  157. Jason R. W. Merrick & Claire A. Dorsey & Bo Wang & Martha Grabowski & John R. Harrald, 2022. "Measuring Prediction Accuracy in a Maritime Accident Warning System," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 819-827, February.
  158. Alexis Gerossier & Robin Girard & George Kariniotakis, 2019. "Modeling and Forecasting Electric Vehicle Consumption Profiles," Energies, MDPI, vol. 12(7), pages 1-14, April.
  159. Luis Mazorra-Aguiar & Philippe Lauret & Mathieu David & Albert Oliver & Gustavo Montero, 2021. "Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency," Energies, MDPI, vol. 14(6), pages 1-26, March.
  160. Garvit Arora & Shubhangi Tiwari & Ying Wu & Xuan Mei, 2024. "An Exploration to the Correlation Structure and Clustering of Macroeconomic Variables," Papers 2401.10162, arXiv.org, revised May 2024.
  161. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
  162. Olson, Luke M. & Qi, Min & Zhang, Xiaofei & Zhao, Xinlei, 2021. "Machine learning loss given default for corporate debt," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 144-159.
  163. Philippe Goulet Coulombe, 2020. "To Bag is to Prune," Papers 2008.07063, arXiv.org, revised Sep 2024.
  164. Aghabozorgi, Farshad & Khayyambashi, Mohammad Reza, 2018. "A new similarity measure for link prediction based on local structures in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 12-23.
  165. Tuba Bostan, 2024. "Generating a Landslide Susceptibility Map Using Integrated Meta-Heuristic Optimization and Machine Learning Models," Sustainability, MDPI, vol. 16(21), pages 1-27, October.
  166. Zhang, Limao & Lin, Penghui, 2021. "Multi-objective optimization for limiting tunnel-induced damages considering uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
  167. Abdou, Hussein A. & Elamer, Ahmed A. & Abedin, Mohammad Zoynul & Ibrahim, Bassam A., 2024. "The impact of oil and global markets on Saudi stock market predictability: A machine learning approach," Energy Economics, Elsevier, vol. 132(C).
  168. Seungwook Kim & Daeyoung Choi & Eunjung Lee & Wonjong Rhee, 2017. "Churn prediction of mobile and online casual games using play log data," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-19, July.
  169. Romero Martínez, Mariano & Carmona Ibáñez, Pedro & Pozuelo Campillo, José, 2021. "Utilidad del Deep Learning en la predicción del fracaso empresarial en el ámbito europeo || The usefulness of Deep Learning in the prediction of business failure at the European level," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 32(1), pages 392-414, December.
  170. Enwei Zhu & Stanislav Sobolevsky, 2018. "House Price Modeling with Digital Census," Papers 1809.03834, arXiv.org.
  171. Joseph Sexton & Petter Laake, 2007. "Boosted Regression Trees with Errors in Variables," Biometrics, The International Biometric Society, vol. 63(2), pages 586-592, June.
  172. 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).
  173. 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).
  174. 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.
  175. 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.
  176. 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.
  177. 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.
  178. 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.
  179. 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.
  180. 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).
  181. 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.
  182. 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.
  183. 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.
  184. 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.
  185. Jo~ao B. Assunc{c}~ao & Pedro Afonso Fernandes, 2024. "The Surprising Robustness of Partial Least Squares," Papers 2409.05713, arXiv.org.
  186. 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.
  187. 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.
  188. 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.
  189. 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.
  190. 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.
  191. 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).
  192. 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.
  193. 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.
  194. 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.
  195. 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.
  196. Manon Venet & Margarida Sa Ribeiro & Elodie Décembre & Alicia Bellomo & Garima Joshi & Célia Nuovo & Marine Villard & David Cluet & Magali Perret & Rémi Pescamona & Helena Paidassi & Thierry Walzer & , 2023. "Severe COVID-19 patients have impaired plasmacytoid dendritic cell-mediated control of SARS-CoV-2," Nature Communications, Nature, vol. 14(1), pages 1-21, December.
  197. Hughes, Neal & Soh, Wei Ying & Lawson, Kenton & Lu, Michael, 2022. "Improving the performance of micro-simulation models with machine learning: The case of Australian farms," Economic Modelling, Elsevier, vol. 115(C).
  198. Evangelos Spiliotis & Spyros Makridakis & Artemios-Anargyros Semenoglou & Vassilios Assimakopoulos, 2022. "Comparison of statistical and machine learning methods for daily SKU demand forecasting," Operational Research, Springer, vol. 22(3), pages 3037-3061, July.
  199. Mehmet Güney Celbiş & Pui-Hang Wong & Karima Kourtit & Peter Nijkamp, 2021. "Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach," Sustainability, MDPI, vol. 13(23), pages 1-29, December.
  200. Hai Sun & Jin Wang & Wentao Ye, 2021. "A Data Augmentation-Based Evaluation System for Regional Direct Economic Losses of Storm Surge Disasters," IJERPH, MDPI, vol. 18(6), pages 1-23, March.
  201. Zhang, Chun-Xia & Zhang, Jiang-She, 2008. "A local boosting algorithm for solving classification problems," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1928-1941, January.
  202. Majid Bazarbash, 2019. "FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk," IMF Working Papers 2019/109, International Monetary Fund.
  203. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
  204. Behzad Vahedi & Morteza Karimzadeh & Hamidreza Zoraghein, 2021. "Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
  205. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
  206. Zhao, Shuting & Wu, Lifeng & Xiang, Youzhen & Dong, Jianhua & Li, Zhen & Liu, Xiaoqiang & Tang, Zijun & Wang, Han & Wang, Xin & An, Jiaqi & Zhang, Fucang & Li, Zhijun, 2022. "Coupling meteorological stations data and satellite data for prediction of global solar radiation with machine learning models," Renewable Energy, Elsevier, vol. 198(C), pages 1049-1064.
  207. Wenbo Wu & Jiaqi Chen & Zhibin (Ben) Yang & Michael L. Tindall, 2021. "A Cross-Sectional Machine Learning Approach for Hedge Fund Return Prediction and Selection," Management Science, INFORMS, vol. 67(7), pages 4577-4601, July.
  208. Laura Alessandretti & Abeer ElBahrawy & Luca Maria Aiello & Andrea Baronchelli, 2018. "Anticipating Cryptocurrency Prices Using Machine Learning," Complexity, Hindawi, vol. 2018, pages 1-16, November.
  209. Ervin Shan Khai Tiu & Yuk Feng Huang & Jing Lin Ng & Nouar AlDahoul & Ali Najah Ahmed & Ahmed Elshafie, 2022. "An evaluation of various data pre-processing techniques with machine learning models for water level prediction," 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. 110(1), pages 121-153, January.
  210. Wei Liu & Yoshihisa Suzuki & Shuyi Du, 2024. "Forecasting the Stock Price of Listed Innovative SMEs Using Machine Learning Methods Based on Bayesian optimization: Evidence from China," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 2035-2068, May.
  211. Kusiak, Andrew & Zhang, Zijun, 2012. "Control of wind turbine power and vibration with a data-driven approach," Renewable Energy, Elsevier, vol. 43(C), pages 73-82.
  212. Mukesh Kumar & Saurabh Singhal & Shashi Shekhar & Bhisham Sharma & Gautam Srivastava, 2022. "Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning," Sustainability, MDPI, vol. 14(21), pages 1-26, October.
  213. 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.
  214. Andrea Sciandra & Alessio Surian & Livio Finos, 2021. "Supervised Machine Learning Methods to Disclose Action and Information in “U.N. 2030 Agenda” Social Media Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 689-699, August.
  215. Leo Guelman & Montserrat Guillen & Ana M. Pérez-Marín, 2014. "Optimal personalized treatment rules for marketing interventions: A review of methods, a new proposal, and an insurance case study," Working Papers 2014-06, Universitat de Barcelona, UB Riskcenter.
  216. 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".
  217. 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.
  218. 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.
  219. 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.
  220. Stan Hatko, 2017. "The Bank of Canada 2015 Retailer Survey on the Cost of Payment Methods: Nonresponse," Technical Reports 107, Bank of Canada.
  221. 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.
  222. 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.
  223. 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.
  224. 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.
  225. 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).
  226. A. R. Provenzano & D. Trifir`o & A. Datteo & L. Giada & N. Jean & A. Riciputi & G. Le Pera & M. Spadaccino & L. Massaron & C. Nordio, 2020. "Machine Learning approach for Credit Scoring," Papers 2008.01687, arXiv.org.
  227. Miten Mistry & Dimitrios Letsios & Gerhard Krennrich & Robert M. Lee & Ruth Misener, 2021. "Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 1103-1119, July.
  228. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
  229. Khoa Hoang & Robert Faff, 2021. "Is the ex‐ante equity risk premium always positive? Evidence from a new conditional expectations model," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(1), pages 95-124, March.
  230. Shan, Pengyue & Bai, Xue & Jiang, Qi & Chen, Yunjian & Lu, Sen & Song, Pei & Jia, Zepeng & Xiao, Taiyang & Han, Yang & Wang, Yazhou & Liu, Tong & Cui, Hong & Feng, Rong & Kang, Qin & Liang, Zhiyong & , 2023. "Bilayer MN4-O-MN4 by bridge-bonded oxygen ligands: Machine learning to accelerate the design of bifunctional electrocatalysts," Renewable Energy, Elsevier, vol. 203(C), pages 445-454.
  231. Gi-Wook Cha & Won-Hwa Hong & Se-Hyu Choi & Young-Chan Kim, 2023. "Developing an Optimal Ensemble Model to Estimate Building Demolition Waste Generation Rate," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
  232. Xiaoqin Li & Xiaomei Wu & Mingzhuang Sun & Shengqiao Yang & Weikun Song, 2022. "A Novel Intelligent Leakage Monitoring-Warning System for Sustainable Rural Drinking Water Supply," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
  233. Marcin Chlebus & Zuzanna Osika, 2020. "Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools," Working Papers 2020-15, Faculty of Economic Sciences, University of Warsaw.
  234. Tsao, C. Andy & Chang, Yuan-chin Ivan, 2007. "A stochastic approximation view of boosting," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 325-334, September.
  235. Mehmet Güney Celbiş, 2021. "A machine learning approach to rural entrepreneurship," Papers in Regional Science, Wiley Blackwell, vol. 100(4), pages 1079-1104, August.
  236. Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2007. "Robust learning from bites for data mining," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 347-361, September.
  237. Ballings, Michel & Van den Poel, Dirk, 2015. "CRM in social media: Predicting increases in Facebook usage frequency," European Journal of Operational Research, Elsevier, vol. 244(1), pages 248-260.
  238. Tao, Tao & Wang, Jueyu & Cao, Xinyu, 2020. "Exploring the non-linear associations between spatial attributes and walking distance to transit," Journal of Transport Geography, Elsevier, vol. 82(C).
  239. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2023. "Variation in the infant health effects of the women, infants, and children program by predicted risk using novel machine learning methods," Health Economics, John Wiley & Sons, Ltd., vol. 32(1), pages 194-217, January.
  240. Shruti Sachdeva & Bijendra Kumar, 2020. "A Comparative Study between Frequency Ratio Model and Gradient Boosted Decision Trees with Greedy Dimensionality Reduction in Groundwater Potential Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4593-4615, December.
  241. Ana Vukicevic & Milan Vukicevic & Sandro Radovanovic & Boris Delibasic, 2022. "BargCrEx: A System for Bargaining Based Aggregation of Crowd and Expert Opinions in Crowdsourcing," Group Decision and Negotiation, Springer, vol. 31(4), pages 789-818, August.
  242. Notz, Pascal M. & Pibernik, Richard, 2024. "Explainable subgradient tree boosting for prescriptive analytics in operations management," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1119-1133.
  243. Reza Zakerinejad & Michael Maerker, 2015. "An integrated assessment of soil erosion dynamics with special emphasis on gully erosion in the Mazayjan basin, southwestern Iran," 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(1), pages 25-50, November.
  244. Zhu, Siying & Zhu, Feng, 2019. "Cycling comfort evaluation with instrumented probe bicycle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 217-231.
  245. E. D. Attanasi & T. C. Coburn & P. A. Freeman, 2024. "Machine learning approaches to identify lithium concentration in petroleum produced waters," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 37(3), pages 477-497, September.
  246. Tao, Tao & Cao, Jason, 2023. "Exploring nonlinear and collective influences of regional and local built environment characteristics on travel distances by mode," Journal of Transport Geography, Elsevier, vol. 109(C).
  247. Filgueiras, Roberto & Almeida, Thomé Simpliciano & Mantovani, Everardo Chartuni & Dias, Santos Henrique Brant & Fernandes-Filho, Elpídio Inácio & da Cunha, Fernando França & Venancio, Luan Peroni, 2020. "Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data," Agricultural Water Management, Elsevier, vol. 241(C).
  248. Federico Divina & Aude Gilson & Francisco Goméz-Vela & Miguel García Torres & José F. Torres, 2018. "Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting," Energies, MDPI, vol. 11(4), pages 1-31, April.
  249. Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.
  250. Min-Kyu Baek & Duehee Lee, 2017. "Spatial and Temporal Day-Ahead Total Daily Solar Irradiation Forecasting: Ensemble Forecasting Based on the Empirical Biasing," Energies, MDPI, vol. 11(1), pages 1-18, December.
  251. Magboul M. Sulieman & Fuat Kaya & Mohammed A. Elsheikh & Levent Başayiğit & Rosa Francaviglia, 2023. "Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions," Land, MDPI, vol. 12(9), pages 1-22, August.
  252. Culp, Mark & Johnson, Kjell & Michailides, George, 2006. "ada: An R Package for Stochastic Boosting," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 17(i02).
  253. Dhivya Elavarasan & Durai Raj Vincent P M & Kathiravan Srinivasan & Chuan-Yu Chang, 2020. "A Hybrid CFS Filter and RF-RFE Wrapper-Based Feature Extraction for Enhanced Agricultural Crop Yield Prediction Modeling," Agriculture, MDPI, vol. 10(9), pages 1-27, September.
  254. 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.
  255. Delen, Dursun & Zolbanin, Hamed M., 2018. "The analytics paradigm in business research," Journal of Business Research, Elsevier, vol. 90(C), pages 186-195.
  256. 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.
  257. 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.
  258. 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.
  259. 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.
  260. 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.
  261. 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.
  262. 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.
  263. 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).
  264. 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.
  265. 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.
  266. 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.
  267. 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.
  268. 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.
  269. Aurélie Lemmens & Sunil Gupta, 2020. "Managing Churn to Maximize Profits," Marketing Science, INFORMS, vol. 39(5), pages 956-973, September.
  270. 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.
  271. 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.
  272. 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.
  273. 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.
  274. Cáceres, Neila & Malone, Samuel W., 2013. "Forecasting leadership transitions around the world," International Journal of Forecasting, Elsevier, vol. 29(4), pages 575-591.
  275. 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.
  276. 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.
  277. 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.
  278. 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.
  279. Catherine Ikae & Jacques Savoy, 2022. "Gender identification on Twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(1), pages 58-69, January.
  280. Wang, Jingjing & Lou, Yu & Wang, Wentao & Liu, Suyi & Zhang, Haohui & Hui, Xin & Wang, Yunling & Yan, Haijun & Maes, Wouter H., 2024. "A robust model for diagnosing water stress of winter wheat by combining UAV multispectral and thermal remote sensing," Agricultural Water Management, Elsevier, vol. 291(C).
  281. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2024. "The infant health effects of doulas: Leveraging big data and machine learning to inform cost‐effective targeting," Health Economics, John Wiley & Sons, Ltd., vol. 33(6), pages 1387-1411, June.
  282. Guo, Peiyang & Lam, Jacqueline C.K. & Li, Victor O.K., 2019. "Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach," Applied Energy, Elsevier, vol. 235(C), pages 900-913.
  283. Xiaoshan Su & Manying Bai, 2020. "Stochastic gradient boosting frequency-severity model of insurance claims," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-24, August.
  284. Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
  285. Jianqing Fan & Lingzhou Xue & Hui Zou, 2016. "Multitask Quantile Regression Under the Transnormal Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1726-1735, October.
  286. Martijn Kagie & Michiel Van Wezel, 2007. "Hedonic price models and indices based on boosting applied to the Dutch housing market," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(3‐4), pages 85-106, July.
  287. Matthias Bogaert & Michel Ballings & Dirk Van den Poel, 2018. "Evaluating the importance of different communication types in romantic tie prediction on social media," Annals of Operations Research, Springer, vol. 263(1), pages 501-527, April.
  288. Dursun Delen & Hamed M. Zolbanin & Durand Crosby & David Wright, 2021. "To imprison or not to imprison: an analytics model for drug courts," Annals of Operations Research, Springer, vol. 303(1), pages 101-124, August.
  289. 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.
  290. 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.
  291. 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.
  292. 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).
  293. 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.
  294. Doruk Cengiz & Arindrajit Dube & Attila S. Lindner & David Zentler-Munro, 2021. "Seeing Beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes," NBER Working Papers 28399, National Bureau of Economic Research, Inc.
  295. 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.
  296. 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).
  297. 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.
  298. 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.).
  299. 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.
  300. Dawei Cheng & Zhibin Niu & Yi Tu & Liqing Zhang, 2017. "Prediction defaults for networked-guarantee loans," Papers 1702.04642, arXiv.org, revised Jun 2020.
  301. Nhat-Duc Hoang, 2022. "Machine Learning-Based Estimation of the Compressive Strength of Self-Compacting Concrete: A Multi-Dataset Study," Mathematics, MDPI, vol. 10(20), pages 1-20, October.
  302. Emrich, Eike & Pierdzioch, Christian, 2015. "Public goods, private consumption, and human-capital formation: On the economics of volunteer labour supply," Working Papers of the European Institute for Socioeconomics 14, European Institute for Socioeconomics (EIS), Saarbrücken.
  303. María Carmen Ruiz-Abellón & Luis Alfredo Fernández-Jiménez & Antonio Guillamón & Alberto Falces & Ana García-Garre & Antonio Gabaldón, 2019. "Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation," Energies, MDPI, vol. 13(1), pages 1-31, December.
  304. Catalin-Emanuel CIOBOTA & Manuela-Violeta TUREATCA, 2022. "Prediction of Business Bankruptcy with the Help of Extreme Gradient Increase," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 3, pages 178-185.
  305. Riccardo Aiolfi & Nicola Moreni & Marco Bianchetti & Marco Scaringi & Filippo Fogliani, 2021. "Learning Bermudans," Papers 2105.00655, arXiv.org.
  306. Sarah Hadri & Mehdi Najib & Mohamed Bakhouya & Youssef Fakhri & Mohamed El Arroussi, 2021. "Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings," Energies, MDPI, vol. 14(18), pages 1-17, September.
  307. Sandra Ceballos-Santos & Jaime González-Pardo & David C. Carslaw & Ana Santurtún & Miguel Santibáñez & Ignacio Fernández-Olmo, 2021. "Meteorological Normalisation Using Boosted Regression Trees to Estimate the Impact of COVID-19 Restrictions on Air Quality Levels," IJERPH, MDPI, vol. 18(24), pages 1-18, December.
  308. Tomasz Pisula, 2020. "An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship," JRFM, MDPI, vol. 13(2), pages 1-35, February.
  309. Gerald Forkuor & Ozias K L Hounkpatin & Gerhard Welp & Michael Thiel, 2017. "High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-21, January.
  310. Mika Ylinen & Mikko Ranta, 2024. "Employer ratings in social media and firm performance: Evidence from an explainable machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 247-276, March.
  311. Joseph L. Breeden & Eugenia Leonova, 2021. "Creating Unbiased Machine Learning Models by Design," JRFM, MDPI, vol. 14(11), pages 1-15, November.
  312. Gareth J Williams & Greta S Aeby & Rebecca O M Cowie & Simon K Davy, 2010. "Predictive Modeling of Coral Disease Distribution within a Reef System," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-10, February.
  313. Akash Malhotra & Mayank Maloo, 2017. "Understanding food inflation in India: A Machine Learning approach," Papers 1701.08789, arXiv.org.
  314. Xiaoqi Wang & Jianfu Cao & Lerui Chen & Heyu Hu, 2020. "The optimized algorithm based on machine learning for inverse kinematics of two painting robots with non-spherical wrist," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-23, April.
  315. Damilola Ologunagba & Shyam Kattel, 2020. "Machine Learning Prediction of Surface Segregation Energies on Low Index Bimetallic Surfaces," Energies, MDPI, vol. 13(9), pages 1-13, May.
  316. Dimitris Bertsimas & Michael Lingzhi Li & Ioannis Ch Paschalidis & Taiyao Wang, 2020. "Prescriptive analytics for reducing 30-day hospital readmissions after general surgery," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-12, September.
  317. Zhang, Junni L. & Härdle, Wolfgang K., 2010. "The Bayesian Additive Classification Tree applied to credit risk modelling," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1197-1205, May.
  318. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha, 2019. "Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm," Energies, MDPI, vol. 12(6), pages 1-13, March.
  319. Stefan Lessmann & Stefan Voß, 2010. "Customer-Centric Decision Support," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(2), pages 79-93, April.
  320. Jiyuan Zhang & Qihong Feng & Xianmin Zhang & Qiujia Hu & Jiaosheng Yang & Ning Wang, 2020. "A Novel Data-Driven Method to Estimate Methane Adsorption Isotherm on Coals Using the Gradient Boosting Decision Tree: A Case Study in the Qinshui Basin, China," Energies, MDPI, vol. 13(20), pages 1-21, October.
  321. Yahia Mutalib Tofiq & Sarmad Dashti Latif & Ali Najah Ahmed & Pavitra Kumar & Ahmed El-Shafie, 2022. "Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5999-6016, December.
  322. Dietrichson, Jens & Klokker, Rasmus H., 2024. "Predicting preschool problems," Children and Youth Services Review, Elsevier, vol. 161(C).
  323. Fatemeh Moodi & Amir Jahangard-Rafsanjani & Sajad Zarifzadeh, 2023. "Feature selection and regression methods for stock price prediction using technical indicators," Papers 2310.09903, arXiv.org, revised Nov 2023.
  324. Simsekler, Mecit Can Emre & Rodrigues, Clarence & Qazi, Abroon & Ellahham, Samer & Ozonoff, Al, 2021. "A comparative study of patient and staff safety evaluation using tree-based machine learning algorithms," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
  325. Rachel Sippy & Daniel F Farrell & Daniel A Lichtenstein & Ryan Nightingale & Megan A Harris & Joseph Toth & Paris Hantztidiamantis & Nicholas Usher & Cinthya Cueva Aponte & Julio Barzallo Aguilar & An, 2020. "Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(2), pages 1-20, February.
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