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A random forest guided tour

Citations

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Cited by:

  1. José A. Ferreira, 2022. "Models under which random forests perform badly; consequences for applications," Computational Statistics, Springer, vol. 37(4), pages 1839-1854, September.
  2. Blanquero, Rafael & Carrizosa, Emilio & Molero-Río, Cristina & Romero Morales, Dolores, 2020. "Sparsity in optimal randomized classification trees," European Journal of Operational Research, Elsevier, vol. 284(1), pages 255-272.
  3. Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).
  4. Yasheng Chen & Zhuojun Wu, 2022. "Financial Fraud Detection of Listed Companies in China: A Machine Learning Approach," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
  5. Xuechen Li & Xinfang Ma & Fengchao Xiao & Fei Wang & Shicheng Zhang, 2020. "Application of Gated Recurrent Unit (GRU) Neural Network for Smart Batch Production Prediction," Energies, MDPI, vol. 13(22), pages 1-22, November.
  6. Lei, Guoqing & Zeng, Wenzhi & Yu, Jin & Huang, Jiesheng, 2023. "A comparison of physical-based and machine learning modeling for soil salt dynamics in crop fields," Agricultural Water Management, Elsevier, vol. 277(C).
  7. Villacis, Alexis & Badruddoza, Syed & Mayorga, Joaquin & Mishra, Ashok K., 2022. "Using Machine Learning to Test the Consistency of Food Insecurity Measures," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322472, Agricultural and Applied Economics Association.
  8. Michael R. Appleton & Alexandre Courtiol & Lucy Emerton & James L. Slade & Andrew Tilker & Lauren C. Warr & Mónica Álvarez Malvido & James R. Barborak & Louise Bruin & Rosalie Chapple & Jennifer C. Da, 2022. "Protected area personnel and ranger numbers are insufficient to deliver global expectations," Nature Sustainability, Nature, vol. 5(12), pages 1100-1110, December.
  9. Khan, Muhammad Asif & Segovia, Juan E.Trinidad & Bhatti, M.Ishaq & Kabir, Asif, 2023. "Corporate vulnerability in the US and China during COVID-19: A machine learning approach," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
  10. Farnaz Yarveysi & Atieh Alipour & Hamed Moftakhari & Keighobad Jafarzadegan & Hamid Moradkhani, 2023. "Block-level vulnerability assessment reveals disproportionate impacts of natural hazards across the conterminous United States," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
  11. Tomasz Ząbkowski & Krzysztof Gajowniczek & Grzegorz Matejko & Jacek Brożyna & Grzegorz Mentel & Małgorzata Charytanowicz & Jolanta Jarnicka & Anna Olwert & Weronika Radziszewska, 2023. "Changing Electricity Tariff—An Empirical Analysis Based on Commercial Customers’ Data from Poland," Energies, MDPI, vol. 16(19), pages 1-17, September.
  12. Daniel Goller & Michael C. Knaus & Michael Lechner & Gabriel Okasa, 2021. "Predicting match outcomes in football by an Ordered Forest estimator," Chapters, in: Ruud H. Koning & Stefan Kesenne (ed.), A Modern Guide to Sports Economics, chapter 22, pages 335-355, Edward Elgar Publishing.
  13. Valeria D’Amato & Rita D’Ecclesia & Susanna Levantesi, 2022. "ESG score prediction through random forest algorithm," Computational Management Science, Springer, vol. 19(2), pages 347-373, June.
  14. Yan, Ran & Wang, Shuaian & Fagerholt, Kjetil, 2020. "A semi-“smart predict then optimize” (semi-SPO) method for efficient ship inspection," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 100-125.
  15. Kara Karpman & Sumanta Basu & David Easley, 2022. "Learning Financial Networks with High-frequency Trade Data," Papers 2208.03568, arXiv.org.
  16. Asmae Chakir & Mohamed Tabaa, 2024. "Hybrid Renewable Production Scheduling for a PV–Wind-EV-Battery Architecture Using Sequential Quadratic Programming and Long Short-Term Memory–K-Nearest Neighbors Learning for Smart Buildings," Sustainability, MDPI, vol. 16(5), pages 1-24, March.
  17. Giacomo Caterini, 2018. "Classifying Firms with Text Mining," DEM Working Papers 2018/09, Department of Economics and Management.
  18. 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.
  19. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
  20. Sylvain Arlot & Robin Genuer, 2016. "Comments on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 228-238, June.
  21. Raman Pall & Yvan Gauthier & Sofia Auer & Walid Mowaswes, 2023. "Predicting drug shortages using pharmacy data and machine learning," Health Care Management Science, Springer, vol. 26(3), pages 395-411, September.
  22. Ningyuan Chen & Guillermo Gallego & Zhuodong Tang, 2019. "The Use of Binary Choice Forests to Model and Estimate Discrete Choices," Papers 1908.01109, arXiv.org, revised Apr 2024.
  23. Panagiotis Panagiotidis & Kyriakos Giannakis & Nikolaos Angelopoulos & Angelos Liapis, 2021. "Shipping Accidents Dataset: Data-Driven Directions for Assessing Accident’s Impact and Improving Safety Onboard," Data, MDPI, vol. 6(12), pages 1-19, December.
  24. Kayo Murakami & Hideki Shimada & Yoshiaki Ushifusa & Takanori Ida, 2022. "Heterogeneous Treatment Effects Of Nudge And Rebate: Causal Machine Learning In A Field Experiment On Electricity Conservation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1779-1803, November.
  25. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
  26. Amin, Modhurima Dey & Badruddoza, Syed & McCluskey, Jill J., 2021. "Predicting access to healthful food retailers with machine learning," Food Policy, Elsevier, vol. 99(C).
  27. Kiziridis, Diogenis A. & Mastrogianni, Anna & Pleniou, Magdalini & Tsiftsis, Spyros & Xystrakis, Fotios & Tsiripidis, Ioannis, 2023. "Improving the predictive performance of CLUE-S by extending demand to land transitions: The trans-CLUE-S model," Ecological Modelling, Elsevier, vol. 478(C).
  28. Fuat Kaya & Calogero Schillaci & Ali Keshavarzi & Levent Başayiğit, 2022. "Predictive Mapping of Electrical Conductivity and Assessment of Soil Salinity in a Western Türkiye Alluvial Plain," Land, MDPI, vol. 11(12), pages 1-21, November.
  29. David Puga-Gil & Gonzalo Astray & Enrique Barreiro & Juan F. Gálvez & Juan Carlos Mejuto, 2022. "Global Solar Irradiation Modelling and Prediction Using Machine Learning Models for Their Potential Use in Renewable Energy Applications," Mathematics, MDPI, vol. 10(24), pages 1-21, December.
  30. Nametso Matomela & Tianxin Li & Peng Zhang & Harrison Odion Ikhumhen & Namir Domingos Raimundo Lopes, 2023. "Role of Landscape and Land-Use Transformation on Nonpoint Source Pollution and Runoff Distribution in the Dongsheng Basin, China," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
  31. Jie Shi & Arno P. J. M. Siebes & Siamak Mehrkanoon, 2023. "TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start," Papers 2311.18749, arXiv.org.
  32. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
  33. 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.
  34. Pourkhanali, Armin & Kholghi, Donya & Llorca, Manuel & Jamasb, Tooraj, 2023. "Persistent and Transient Energy Poverty: A Multi-Level Analysis in Spain," Working Papers 9-2023, Copenhagen Business School, Department of Economics.
  35. Xie He & Amir Ghasemian & Eun Lee & Aaron Clauset & Peter J. Mucha, 2024. "Sequential stacking link prediction algorithms for temporal networks," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  36. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
  37. Eric Cebekhulu & Adeiza James Onumanyi & Sherrin John Isaac, 2022. "Performance Analysis of Machine Learning Algorithms for Energy Demand–Supply Prediction in Smart Grids," Sustainability, MDPI, vol. 14(5), pages 1-26, February.
  38. Schnaubelt, Matthias & Fischer, Thomas G. & Krauss, Christopher, 2020. "Separating the signal from the noise – Financial machine learning for Twitter," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).
  39. Faming Wang & Ronnel B. King & Shing On Leung, 2022. "Beating the odds: Identifying the top predictors of resilience among Hong Kong students," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 15(5), pages 1921-1944, October.
  40. Ezgi Gülenç Bayirli & Atabey Kaygun & Ersoy Öz, 2023. "An Analysis of PISA 2018 Mathematics Assessment for Asia-Pacific Countries Using Educational Data Mining," Mathematics, MDPI, vol. 11(6), pages 1-23, March.
  41. Kushanav Bhuyan & Kamal Rana & Joaquin V. Ferrer & Fabrice Cotton & Ugur Ozturk & Filippo Catani & Nishant Malik, 2024. "Landslide topology uncovers failure movements," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  42. Gérard Biau & Erwan Scornet & Johannes Welbl, 2019. "Neural Random Forests," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(2), pages 347-386, December.
  43. Pedro Forquesato, 2022. "Who Benefits from Political Connections in Brazilian Municipalities," Papers 2204.09450, arXiv.org.
  44. Xiaoyue Hu & Jie Hu, 2021. "A Classification Analysis of the High and Low Levels of Global Competence of Secondary Students: Insights from 25 Countries/Regions," Sustainability, MDPI, vol. 13(19), pages 1-17, October.
  45. Andree,Bo Pieter Johannes & Chamorro Elizondo,Andres Fernando & Kraay,Aart C. & Spencer,Phoebe Girouard & Wang,Dieter, 2020. "Predicting Food Crises," Policy Research Working Paper Series 9412, The World Bank.
  46. Valente, Marica, 2023. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
  47. Makariou, Despoina & Barrieu, Pauline & Chen, Yining, 2021. "A random forest based approach for predicting spreads in the primary catastrophe bond market," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 140-162.
  48. Bourdouxhe, Axel & Wibail, Lionel & Claessens, Hugues & Dufrêne, Marc, 2023. "Modeling potential natural vegetation: A new light on an old concept to guide nature conservation in fragmented and degraded landscapes," Ecological Modelling, Elsevier, vol. 481(C).
  49. Park, Beomjin & Park, Changyi, 2023. "Multiclass Laplacian support vector machine with functional analysis of variance decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
  50. Urfels, Anton & Mausch, Kai & Harris, Dave & McDonald, Andrew J. & Kishore, Avinash & Balwinder-Singh, & van Halsema, Gerardo & Struik, Paul C. & Craufurd, Peter & Foster, Timothy & Singh, Vartika & K, 2023. "Farm size limits agriculture's poverty reduction potential in Eastern India even with irrigation-led intensification," Agricultural Systems, Elsevier, vol. 207(C).
  51. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
  52. Fang Wang & Weijie Du & Hongxiang Feng & Yun Ye & Manel Grifoll & Guiyun Liu & Pengjun Zheng, 2023. "Identification of Risk Influential Factors for Fishing Vessel Accidents Using Claims Data from Fishery Mutual Insurance Association," Sustainability, MDPI, vol. 15(18), pages 1-24, September.
  53. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
  54. Ye Tian & Xiaobai Yao, 2022. "Urban form, traffic volume, and air quality: A spatiotemporal stratified approach," Environment and Planning B, , vol. 49(1), pages 92-113, January.
  55. Diogenis A. Kiziridis & Anna Mastrogianni & Magdalini Pleniou & Spyros Tsiftsis & Fotios Xystrakis & Ioannis Tsiripidis, 2023. "Simulating Future Land Use and Cover of a Mediterranean Mountainous Area: The Effect of Socioeconomic Demands and Climatic Changes," Land, MDPI, vol. 12(1), pages 1-23, January.
  56. Daniel Boller & Michael Lechner & Gabriel Okasa, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers 2104.04601, arXiv.org.
  57. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
  58. Benjamin David, 2017. "Model economic phenomena with CART and Random Forest algorithms," Working Papers hal-04141619, HAL.
  59. Gao, Qishuo & Shi, Vivien & Pettit, Christopher & Han, Hoon, 2022. "Property valuation using machine learning algorithms on statistical areas in Greater Sydney, Australia," Land Use Policy, Elsevier, vol. 123(C).
  60. Jorge Antunes & Peter Wanke & Thiago Fonseca & Yong Tan, 2023. "Do ESG Risk Scores Influence Financial Distress? Evidence from a Dynamic NDEA Approach," Sustainability, MDPI, vol. 15(9), pages 1-32, May.
  61. 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.
  62. Akshita Bassi & Aditya Manchanda & Rajwinder Singh & Mahesh Patel, 2023. "A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete," 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. 118(1), pages 209-238, August.
  63. Benjamin David, 2017. "Model economic phenomena with CART and Random Forest algorithms," EconomiX Working Papers 2017-46, University of Paris Nanterre, EconomiX.
  64. Yashon O. Ouma & Boipuso Nkwae & Phillimon Odirile & Ditiro B. Moalafhi & George Anderson & Bhagabat Parida & Jiaguo Qi, 2024. "Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus," Sustainability, MDPI, vol. 16(4), pages 1-30, February.
  65. Díaz, Santiago & Carta, José A. & Matías, José M., 2018. "Performance assessment of five MCP models proposed for the estimation of long-term wind turbine power outputs at a target site using three machine learning techniques," Applied Energy, Elsevier, vol. 209(C), pages 455-477.
  66. Max Biggs & Rim Hariss & Georgia Perakis, 2023. "Constrained optimization of objective functions determined from random forests," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 397-415, February.
  67. Ahmad Saeed Mohammad & Musab T. S. Al-Kaltakchi & Jabir Alshehabi Al-Ani & Jonathon A. Chambers, 2023. "Comprehensive Evaluations of Student Performance Estimation via Machine Learning," Mathematics, MDPI, vol. 11(14), pages 1-16, July.
  68. Elham Rahmani & Mohammad Khatami & Emma Stephens, 2024. "Using Probabilistic Machine Learning Methods to Improve Beef Cattle Price Modeling and Promote Beef Production Efficiency and Sustainability in Canada," Sustainability, MDPI, vol. 16(5), pages 1-19, February.
  69. Zhexiao Lin & Fang Han, 2022. "On regression-adjusted imputation estimators of the average treatment effect," Papers 2212.05424, arXiv.org, revised Jan 2023.
  70. Blanquero, Rafael & Carrizosa, Emilio & Molero-Río, Cristina & Morales, Dolores Romero, 2022. "On sparse optimal regression trees," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1045-1054.
  71. Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," 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. 13(6), pages 3048-3061, December.
  72. Badruddoza, Syed & Amin, Modhurima & McCluskey, Jill, 2019. "Assessing the Importance of an Attribute in a Demand SystemStructural Model versus Machine Learning," Working Papers 2019-5, School of Economic Sciences, Washington State University.
  73. Lyudmyla Kirichenko & Tamara Radivilova & Vitalii Bulakh, 2018. "Machine Learning in Classification Time Series with Fractal Properties," Data, MDPI, vol. 4(1), pages 1-13, December.
  74. Icksung Kim & Woohyun Kim, 2021. "Development and Validation of a Data-Driven Fault Detection and Diagnosis System for Chillers Using Machine Learning Algorithms," Energies, MDPI, vol. 14(7), pages 1-24, April.
  75. Ramin Vakili & Mojdeh Khorsand, 2022. "A Machine Learning-Based Method for Identifying Critical Distance Relays for Transient Stability Studies," Energies, MDPI, vol. 15(23), pages 1-28, November.
  76. Yong-Chao Su & Cheng-Yu Wu & Cheng-Hong Yang & Bo-Sheng Li & Sin-Hua Moi & Yu-Da Lin, 2021. "Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider," Mathematics, MDPI, vol. 9(4), pages 1-16, February.
  77. Jacobovic, Royi & Zuk, Or, 2023. "A phase transition for the probability of being a maximum among random vectors with general iid coordinates," Statistics & Probability Letters, Elsevier, vol. 199(C).
  78. Choi, Yosoon & Nguyen, Hoang & Bui, Xuan-Nam & Nguyen-Thoi, Trung, 2022. "Optimization of haulage-truck system performance for ore production in open-pit mines using big data and machine learning-based methods," Resources Policy, Elsevier, vol. 75(C).
  79. Xue, Shaobo & Ma, Bo & Wang, Chenguang & Li, Zhanbin, 2023. "Identifying key landscape pattern indices influencing the NPP: A case study of the upper and middle reaches of the Yellow River," Ecological Modelling, Elsevier, vol. 484(C).
  80. Rana Muhammad Adnan Ikram & Xinyi Cao & Kulwinder Singh Parmar & Ozgur Kisi & Shamsuddin Shahid & Mohammad Zounemat-Kermani, 2023. "Modeling Significant Wave Heights for Multiple Time Horizons Using Metaheuristic Regression Methods," Mathematics, MDPI, vol. 11(14), pages 1-24, July.
  81. Jiaming Mao & Jingzhi Xu, 2020. "Ensemble Learning with Statistical and Structural Models," Papers 2006.05308, arXiv.org.
  82. Mochen Yang & Edward McFowland & Gordon Burtch & Gediminas Adomavicius, 2022. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 138-155, October.
  83. Colette de Villiers & Cilence Munghemezulu & Zinhle Mashaba-Munghemezulu & George J. Chirima & Solomon G. Tesfamichael, 2023. "Weed Detection in Rainfed Maize Crops Using UAV and PlanetScope Imagery," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
  84. Ruixin Liang & Joanne Yip & Yunli Fan & Jason P. Y. Cheung & Kai-Tsun Michael To, 2022. "Electromyographic Analysis of Paraspinal Muscles of Scoliosis Patients Using Machine Learning Approaches," IJERPH, MDPI, vol. 19(3), pages 1-12, January.
  85. Guillermo Santamaria-Bonfil & Gustavo Arroyo-Figueroa & Miguel A. Zuniga-Garcia & Carlos Gustavo Azcarraga Ramos & Ali Bassam, 2023. "Power Transformer Fault Detection: A Comparison of Standard Machine Learning and autoML Approaches," Energies, MDPI, vol. 17(1), pages 1-22, December.
  86. Brunori, Paolo & Hufe, Paul & Mahler, Daniel Gerszon, 2021. "The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees and Forests," IZA Discussion Papers 14689, Institute of Labor Economics (IZA).
  87. Díaz, Santiago & Carta, José A. & Castañeda, Alberto, 2020. "Influence of the variation of meteorological and operational parameters on estimation of the power output of a wind farm with active power control," Renewable Energy, Elsevier, vol. 159(C), pages 812-826.
  88. Britta L. Schumacher & Emily K. Burchfield & Brennan Bean & Matt A. Yost, 2023. "Leveraging Important Covariate Groups for Corn Yield Prediction," Agriculture, MDPI, vol. 13(3), pages 1-18, March.
  89. Yi Wang & Ronnel King & Shing On Leung, 2023. "Understanding Chinese Students' Well-Being: A Machine Learning Study," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 16(2), pages 581-616, April.
  90. Dai, Hongyan & Xiao, Qin & Chen, Songlin & Zhou, Weihua, 2023. "Data-driven demand forecast for O2O operations: An adaptive hierarchical incremental approach," International Journal of Production Economics, Elsevier, vol. 259(C).
  91. Chunling Sun & Hong Zhang & Lu Xu & Chao Wang & Liutong Li, 2021. "Rice Mapping Using a BiLSTM-Attention Model from Multitemporal Sentinel-1 Data," Agriculture, MDPI, vol. 11(10), pages 1-20, October.
  92. Yazan F. Roumani, 2023. "Sports analytics in the NFL: classifying the winner of the superbowl," Annals of Operations Research, Springer, vol. 325(1), pages 715-730, June.
  93. Darko B. Vukovic & Lubov Spitsina & Ekaterina Gribanova & Vladislav Spitsin & Ivan Lyzin, 2023. "Predicting the Performance of Retail Market Firms: Regression and Machine Learning Methods," Mathematics, MDPI, vol. 11(8), pages 1-23, April.
  94. Nawaf Almaskati, 2022. "Machine learning in finance: Major applications, issues, metrics, and future trends," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 1-32, September.
  95. Schnaubelt, Matthias & Fischer, Thomas G. & Krauss, Christopher, 2018. "Separating the signal from the noise - financial machine learning for Twitter," FAU Discussion Papers in Economics 14/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
  96. Sidney Michelini & Barbora Šedová & Jacob Schewe & Katja Frieler, 2023. "Extreme weather impacts do not improve conflict predictions in Africa," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.
  97. Fuat Kaya & Ali Keshavarzi & Rosa Francaviglia & Gordana Kaplan & Levent Başayiğit & Mert Dedeoğlu, 2022. "Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus," Agriculture, MDPI, vol. 12(7), pages 1-27, July.
  98. Muhammad Raza Farooq & Zezhou Zhang & Linxi Yuan & Xiaodong Liu & Abdul Rehman & Gary S. Bañuelos & Xuebin Yin, 2023. "Influencing Factors on Bioavailability and Spatial Distribution of Soil Selenium in Dry Semi-Arid Area," Agriculture, MDPI, vol. 13(3), pages 1-17, February.
  99. Shahbeik, Hossein & Rafiee, Shahin & Shafizadeh, Alireza & Jeddi, Dorsa & Jafary, Tahereh & Lam, Su Shiung & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2022. "Characterizing sludge pyrolysis by machine learning: Towards sustainable bioenergy production from wastes," Renewable Energy, Elsevier, vol. 199(C), pages 1078-1092.
  100. Diogenis A. Kiziridis & Anna Mastrogianni & Magdalini Pleniou & Elpida Karadimou & Spyros Tsiftsis & Fotios Xystrakis & Ioannis Tsiripidis, 2022. "Acceleration and Relocation of Abandonment in a Mediterranean Mountainous Landscape: Drivers, Consequences, and Management Implications," Land, MDPI, vol. 11(3), pages 1-23, March.
  101. Jules Sadefo Kamdem & Danielle Selambi, 2022. "Cyber-Risk Forecasting using Machine Learning Models and Generalized Extreme Value Distributions," Working Papers hal-03814979, HAL.
  102. Robert Goodspeed & Runzi Wang & Camilla Lizundia & Lingxiao Du & Srishti Jaipuria, 2023. "Incorporating water quality into land use scenario analysis with random forest models," Environment and Planning B, , vol. 50(6), pages 1518-1533, July.
  103. Xiaoming Jin & Weixin Luan & Jun Yang & Wenze Yue & Shulin Wan & Di Yang & Xiangming Xiao & Bing Xue & Yue Dou & Fangzheng Lyu & Shaohua Wang, 2023. "From the coast to the interior: global economic evolution patterns and mechanisms," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
  104. Escribano, Álvaro & Wang, Dandan, 2021. "Mixed random forest, cointegration, and forecasting gasoline prices," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1442-1462.
  105. Hediger, Simon & Michel, Loris & Näf, Jeffrey, 2022. "On the use of random forest for two-sample testing," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
  106. Giuseppe Antonio Catalano & Provvidenza Rita D’Urso & Federico Maci & Claudia Arcidiacono, 2023. "Influence of Parameters in SDM Application on Citrus Presence in Mediterranean Area," Sustainability, MDPI, vol. 15(9), pages 1-20, May.
  107. Hunish Bansal & Basavraj Chinagundi & Prashant Singh Rana & Neeraj Kumar, 2022. "An Ensemble Machine Learning Technique for Detection of Abnormalities in Knee Movement Sustainability," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
  108. Nazmus Sakib Ahmed & Nathan Huynh & Sarah Gassman & Robert Mullen & Charles Pierce & Yuche Chen, 2022. "Predicting Pavement Structural Condition Using Machine Learning Methods," Sustainability, MDPI, vol. 14(14), pages 1-16, July.
  109. Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2023. "Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
  110. Chi-Yo Huang & Chia-Lee Yang & Yi-Hao Hsiao, 2021. "A Novel Framework for Mining Social Media Data Based on Text Mining, Topic Modeling, Random Forest, and DANP Methods," Mathematics, MDPI, vol. 9(17), pages 1-21, August.
  111. Promporn Sornsoongnern & Suthatip Pueboobpaphan & Rattaphol Pueboobpaphan, 2023. "Innovative Dynamic Queue-Length Estimation Using Google Maps Color-Code Data," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
  112. Wassim Le Lann & Gauthier Delozière & Yann Le Lann, 2023. "Greenwashing the Talents: attracting human capital through environmental pledges," SciencePo Working papers Main hal-04140191, HAL.
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