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Rejoinder on: 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. Zhexiao Lin & Fang Han, 2022. "On regression-adjusted imputation estimators of the average treatment effect," Papers 2212.05424, arXiv.org, revised Jan 2023.
  6. 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.
  7. 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).
  8. 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.
  9. 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.
  10. Vittoria La Serra & Emiliano Svezia, 2024. "A supervised record linkage approach for anomaly detection in insurance assets granular data," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4181-4205, October.
  11. 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.
  12. 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.
  13. Aouad, Anthony & Almaksour, Khaled & Abbes, Dhaker, 2024. "Storage management optimization based on electrical consumption and production forecast in a photovoltaic system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 224(PB), pages 128-147.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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).
  20. Spandagos, Constantine & Tovar Reaños, Miguel Angel & Lynch, Muireann Á., 2023. "Energy poverty prediction and effective targeting for just transitions with machine learning," Energy Economics, Elsevier, vol. 128(C).
  21. 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).
  22. 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).
  23. 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.
  24. 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.
  25. 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.
  26. 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).
  27. 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.
  28. 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.
  29. 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.
  30. Kara Karpman & Sumanta Basu & David Easley, 2022. "Learning Financial Networks with High-frequency Trade Data," Papers 2208.03568, arXiv.org.
  31. 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.
  32. Jiaming Mao & Jingzhi Xu, 2020. "Ensemble Learning with Statistical and Structural Models," Papers 2006.05308, arXiv.org.
  33. Giacomo Caterini, 2018. "Classifying Firms with Text Mining," DEM Working Papers 2018/09, Department of Economics and Management.
  34. 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.
  35. Arafat, M.Y. & Hossain, M.J. & Alam, Md Morshed, 2024. "Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
  36. 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.
  37. Pourkhanali, Armin & Khezr, Peyman & Nepal, Rabindra & Jamasb, Tooraj, 2024. "Navigating the crisis: Fuel price caps in the Australian national wholesale electricity market," Energy Economics, Elsevier, vol. 129(C).
  38. 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.
  39. 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).
  40. 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.
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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).
  47. 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.
  48. 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.
  49. 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.
  50. 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.
  51. 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).
  52. Gordeev, Stepan & Steinbach, Sandro, 2024. "Determinants of PTA design: Insights from machine learning," International Economics, Elsevier, vol. 178(C).
  53. 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.
  54. Qiu, Yuhang & Hui, Yunze & Zhao, Pengxiang & Cai, Cheng-Hao & Dai, Baiqian & Dou, Jinxiao & Bhattacharya, Sankar & Yu, Jianglong, 2024. "A novel image expression-driven modeling strategy for coke quality prediction in the smart cokemaking process," Energy, Elsevier, vol. 294(C).
  55. 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.
  56. 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.
  57. 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.
  58. 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.
  59. 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.
  60. 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.
  61. Amin, Modhurima Dey & Badruddoza, Syed & McCluskey, Jill J., 2021. "Predicting access to healthful food retailers with machine learning," Food Policy, Elsevier, vol. 99(C).
  62. Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Economics Working Paper Series 2104, University of St. Gallen, School of Economics and Political Science.
  63. 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).
  64. Ye Tian & Xiaobai Angela Yao & Marguerite Madden & Andrew Grundstein, 2024. "Synergic effects of meteorological factors on urban form-outdoor exercise relationship: A study with crowdsourced data," Journal of Geographical Systems, Springer, vol. 26(1), pages 47-72, January.
  65. 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.
  66. Valente, Marica, 2023. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
  67. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
  68. 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.
  69. Rosario Nastasi & Giovanni Labrini & Simone Salvadori & Daniela Anna Misul, 2024. "Shape Optimization of a Diffusive High-Pressure Turbine Vane Using Machine Learning Tools," Energies, MDPI, vol. 17(22), pages 1-21, November.
  70. 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.
  71. 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.
  72. 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.
  73. 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.
  74. 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.
  75. 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.
  76. Jules Sadefo Kamdem & Danielle Selambi, 2022. "Cyber-Risk Forecasting using Machine Learning Models and Generalized Extreme Value Distributions," Working Papers hal-03814979, HAL.
  77. 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.
  78. 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.
  79. 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.
  80. Escribano, Álvaro & Wang, Dandan, 2021. "Mixed random forest, cointegration, and forecasting gasoline prices," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1442-1462.
  81. 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).
  82. 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.
  83. 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.
  84. 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.
  85. 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.
  86. 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).
  87. 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.
  88. Sebastián Rodríguez & Pablo Cabrera-Barona, 2024. "A machine learning-based assessment of subjective quality of life," Journal of Computational Social Science, Springer, vol. 7(1), pages 451-467, April.
  89. 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.
  90. Matara Caroline Mongina & Nyambane Simpson Osano & Yusuf Amir Okeyo & Ochungo Elisha Akech & Khattak Afaq, 2024. "Classification of Particulate Matter (PM2.5) Concentrations Using Feature Selection and Machine Learning Strategies," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 15(1), pages 85-96.
  91. 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.
  92. 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.
  93. 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.
  94. 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.
  95. 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.
  96. 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.
  97. 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.
  98. 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.
  99. Makariou, Despoina & Barrieu, Pauline & Chen, Yining, 2021. "A random forest based approach for predicting spreads in the primary catastrophe bond market," LSE Research Online Documents on Economics 111529, London School of Economics and Political Science, LSE Library.
  100. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
  101. Pedro Forquesato, 2022. "Who Benefits from Political Connections in Brazilian Municipalities," Papers 2204.09450, arXiv.org.
  102. 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.
  103. Siyoon Kwon & Hyoseob Noh & Il Won Seo & Sung Hyun Jung & Donghae Baek, 2021. "Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
  104. Sebastian Gutierrez Pacheco & Robert Lagacé & Sandrine Hugron & Stéphane Godbout & Line Rochefort, 2021. "Estimation of Daily Water Table Level with Bimonthly Measurements in Restored Ombrotrophic Peatland," Sustainability, MDPI, vol. 13(10), pages 1-21, May.
  105. Wassim Le Lann & Gauthier Delozière & Yann Le Lann, 2023. "Greenwashing the Talents: attracting human capital through environmental pledges," Working Papers hal-04140191, HAL.
  106. 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.
  107. Zhang, Yue & Wang, Yeqin & Zhang, Chu & Qiao, Xiujie & Ge, Yida & Li, Xi & Peng, Tian & Nazir, Muhammad Shahzad, 2024. "State-of-health estimation for lithium-ion battery via an evolutionary Stacking ensemble learning paradigm of random vector functional link and active-state-tracking long–short-term memory neural netw," Applied Energy, Elsevier, vol. 356(C).
  108. Alessandro Bitetto & Paola Cerchiello & Stefano Filomeni & Alessandra Tanda & Barbara Tarantino, 2024. "Can we trust machine learning to predict the credit risk of small businesses?," Review of Quantitative Finance and Accounting, Springer, vol. 63(3), pages 925-954, October.
  109. 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.
  110. 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).
  111. Park, Beomjin & Park, Changyi, 2023. "Multiclass Laplacian support vector machine with functional analysis of variance decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
  112. Boram Choi & Jong Hwan Suh, 2020. "Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea," Sustainability, MDPI, vol. 12(15), pages 1-20, July.
  113. Sylwester Bejger, 2024. "Machine Learning in Cartel Screening—The Case of Parallel Pricing in a Fuel Wholesale Market," Energies, MDPI, vol. 17(16), pages 1-17, August.
  114. Morati Mpalo & Lenyeletse Vincent Basupi & Gizaw Mengistu Tsidu, 2024. "Impacts of Wildlife Artificial Water Provisioning in an African Savannah Ecosystem: A Spatiotemporal Analysis," Land, MDPI, vol. 13(5), pages 1-19, May.
  115. 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).
  116. Nathan Kallus & Xiaojie Mao, 2023. "Stochastic Optimization Forests," Management Science, INFORMS, vol. 69(4), pages 1975-1994, April.
  117. Sean Grimes & David E. Breen, 2023. "A Multi-Agent Approach to Binary Classification Using Swarm Intelligence," Future Internet, MDPI, vol. 15(1), pages 1-27, January.
  118. 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.
  119. 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.
  120. 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.
  121. Emad S. Aljohani & Benaissa Chidmi, 2024. "Analyzing Technical Efficiency in Cereal Production across Selected European Union Countries," Sustainability, MDPI, vol. 16(2), pages 1-27, January.
  122. Lotfi Boudabsa & Damir Filipovi'c, 2022. "Ensemble learning for portfolio valuation and risk management," Papers 2204.05926, arXiv.org.
  123. Daoyong Li & Hengyi Zang & Demiao Yu & Qilin He & Xiaoran Huang, 2023. "Study on the Influence Mechanism and Space Distribution Characteristics of Rail Transit Station Area Accessibility Based on MGWR," IJERPH, MDPI, vol. 20(2), pages 1-21, January.
  124. 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.
  125. Hyun Jin Han & Hae Sun Suh, 2023. "Predicting Unmet Healthcare Needs in Post-Disaster: A Machine Learning Approach," IJERPH, MDPI, vol. 20(19), pages 1-13, September.
  126. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
  127. 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.
  128. F. Leung & M. Law & S. K. Djeng, 2024. "Deterministic modelling of implied volatility in cryptocurrency options with underlying multiple resolution momentum indicator and non-linear machine learning regression algorithm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-25, December.
  129. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
  130. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
  131. Benjamin David, 2017. "Model economic phenomena with CART and Random Forest algorithms," Working Papers hal-04141619, HAL.
  132. García, P., 2022. "A machine learning based control of chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
  133. 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).
  134. Vishal Midya & Chris Gennings, 2024. "Detecting Shape-Based Interactions Among Environmental Chemicals Using an Ensemble of Exposure-Mixture Regression and Interpretable Machine Learning Tools," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 395-415, July.
  135. Wang, Haoyu & Jiang, Bingyou & Lin, Hanyi & Zheng, Haotian & Wang, Yifan & Ji, Ben & Zhou, Yu, 2024. "Analysis of dust pollution characteristics in the respiratory risk zone of the roadway under multiple factors," Energy, Elsevier, vol. 305(C).
  136. Andrei Butnaru & Alexios Mylonas & Nikolaos Pitropakis, 2021. "Towards Lightweight URL-Based Phishing Detection," Future Internet, MDPI, vol. 13(6), pages 1-15, June.
  137. Gadat, Sébastien & Gerchinovitz, Sebastien & Marteau, Clément, 2018. "Optimal functional supervised classification with separation condition," TSE Working Papers 18-904, Toulouse School of Economics (TSE).
  138. Jack Ngarambe & Amina Irakoze & Geun Young Yun & Gon Kim, 2020. "Comparative Performance of Machine Learning Algorithms in the Prediction of Indoor Daylight Illuminances," Sustainability, MDPI, vol. 12(11), pages 1-22, June.
  139. 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.
  140. 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.
  141. 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.
  142. Mohamed Kais Msakni & Anders Risan & Peter Schütz, 2023. "Using machine learning prediction models for quality control: a case study from the automotive industry," Computational Management Science, Springer, vol. 20(1), pages 1-28, December.
  143. Yasemin Lheureux, 2024. "Predictive insights: leveraging Twitter sentiments and machine learning for environmental, social and governance controversy prediction," Journal of Computational Social Science, Springer, vol. 7(1), pages 23-44, April.
  144. Karina Acosta & Juliana Jaramillo-Echeverri & Daniel Lasso & Alejandro Sarasti-Sierra, 2024. "Informalidad municipal en Colombia," Documentos de trabajo sobre Economía Regional y Urbana 327, Banco de la Republica de Colombia.
  145. Benjamin David, 2017. "Model economic phenomena with CART and Random Forest algorithms," EconomiX Working Papers 2017-46, University of Paris Nanterre, EconomiX.
  146. Vala, Roman & Valova, Marie & Drazdilova, Pavla & Krömer, Pavel & Platos, Jan, 2021. "Behaviour associated with the presence of a school sports ground: Visual information for policy makers," Children and Youth Services Review, Elsevier, vol. 128(C).
  147. 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.
  148. 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.
  149. Brunori, Paolo & Hufe, Paul & Mahler, Daniel, 2023. "The roots of inequality: estimating inequality of opportunity from regression trees and forests," LSE Research Online Documents on Economics 118220, London School of Economics and Political Science, LSE Library.
  150. Vuban Chowdhury & Suman Kumar Mitra & Sarah Hernandez, 2024. "Electric Vehicle Usage Patterns in Multi-Vehicle Households in the US: A Machine Learning Study," Sustainability, MDPI, vol. 16(12), pages 1-21, June.
  151. 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.
  152. Antoniadis, Anestis & Lambert-Lacroix, Sophie & Poggi, Jean-Michel, 2021. "Random forests for global sensitivity analysis: A selective review," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
  153. Nassabeh, Mehdi & You, Zhenjiang & Keshavarz, Alireza & Iglauer, Stefan, 2024. "Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection," Energy, Elsevier, vol. 305(C).
  154. Yi Cao & Xue Li, 2022. "Multi-Model Attention Fusion Multilayer Perceptron Prediction Method for Subway OD Passenger Flow under COVID-19," Sustainability, MDPI, vol. 14(21), pages 1-16, November.
  155. Gian Marco Paldino & Fabrizio De Caro & Jacopo De Stefani & Alfredo Vaccaro & Domenico Villacci & Gianluca Bontempi, 2022. "A Digital Twin Approach for Improving Estimation Accuracy in Dynamic Thermal Rating of Transmission Lines," Energies, MDPI, vol. 15(6), pages 1-17, March.
  156. 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.
  157. 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.
  158. Odai Y. Dweekat & Sarah S. Lam & Lindsay McGrath, 2023. "An Integrated System of Braden Scale and Random Forest Using Real-Time Diagnoses to Predict When Hospital-Acquired Pressure Injuries (Bedsores) Occur," IJERPH, MDPI, vol. 20(6), pages 1-18, March.
  159. Maelaynayn El baida & Farid Boushaba & Mimoun Chourak & Mohamed Hosni & Hichame Sabar, 2024. "Classification machine learning models for urban flood hazard mapping: case study of Zaio, NE Morocco," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(11), pages 10013-10041, September.
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