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Feature Selection with the Boruta Package

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Cited by:

  1. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
  2. Hamed Ahmadpour & Ommolbanin Bazrafshan & Elham Rafiei-Sardooi & Hossein Zamani & Thomas Panagopoulos, 2021. "Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection," Sustainability, MDPI, vol. 13(18), pages 1-24, September.
  3. Muhammad Nadim Hanif & Khurrum S. Mughal & Javed Iqbal, 2018. "A Thick ANN Model for Forecasting Inflation," SBP Working Paper Series 99, State Bank of Pakistan, Research Department.
  4. 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).
  5. Esteban Bravo-López & Tomás Fernández Del Castillo & Chester Sellers & Jorge Delgado-García, 2023. "Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods," Land, MDPI, vol. 12(6), pages 1-28, May.
  6. Maryam A. Y. Al-Nesf & Houari B. Abdesselem & Ilham Bensmail & Shahd Ibrahim & Walaa A. H. Saeed & Sara S. I. Mohammed & Almurtada Razok & Hashim Alhussain & Reham M. A. Aly & Muna Al Maslamani & Khal, 2022. "Prognostic tools and candidate drugs based on plasma proteomics of patients with severe COVID-19 complications," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
  7. Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.
  8. Galati, Antonino & Coticchio, Alessandro & Peiró-Signes, Ángel, 2023. "Identifying the factors affecting citizens' willingness to participate in urban forest governance: Evidence from the municipality of Palermo, Italy," Forest Policy and Economics, Elsevier, vol. 155(C).
  9. Bram Janssens & Matthias Bogaert & Mathijs Maton, 2023. "Predicting the next Pogačar: a data analytical approach to detect young professional cycling talents," Annals of Operations Research, Springer, vol. 325(1), pages 557-588, June.
  10. Jamei, Mehdi & Maroufpoor, Saman & Aminpour, Younes & Karbasi, Masoud & Malik, Anurag & Karimi, Bakhtiar, 2022. "Developing hybrid data-intelligent method using Boruta-random forest optimizer for simulation of nitrate distribution pattern," Agricultural Water Management, Elsevier, vol. 270(C).
  11. Jin Li & Maggie Tran & Justy Siwabessy, 2016. "Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-29, February.
  12. Abolfazl Mollalo & Kiara M. Rivera & Behzad Vahedi, 2020. "Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States," IJERPH, MDPI, vol. 17(12), pages 1-13, June.
  13. Zulfiqar Ali & Ijaz Hussain & Muhammad Faisal & Dost Muhammad Khan & Rizwan Niaz & Elsayed Elsherbini Elashkar & Alaa Mohamd Shoukry, 2020. "Propagation of the Multi-Scalar Aggregative Standardized Precipitation Temperature Index and its Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 699-714, January.
  14. Alexander C Keyel & Oliver Elison Timm & P Bryon Backenson & Catharine Prussing & Sarah Quinones & Kathleen A McDonough & Mathias Vuille & Jan E Conn & Philip M Armstrong & Theodore G Andreadis & Laur, 2019. "Seasonal temperatures and hydrological conditions improve the prediction of West Nile virus infection rates in Culex mosquitoes and human case counts in New York and Connecticut," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-32, June.
  15. Tong, Jianfeng & Liu, Zhenxing & Zhang, Yong & Zheng, Xiujuan & Jin, Junyang, 2023. "Improved multi-gate mixture-of-experts framework for multi-step prediction of gas load," Energy, Elsevier, vol. 282(C).
  16. Yingjie Zhu & Jiageng Ma & Fangqing Gu & Jie Wang & Zhijuan Li & Youyao Zhang & Jiani Xu & Yifan Li & Yiwen Wang & Xiangqun Yang, 2023. "Price Prediction of Bitcoin Based on Adaptive Feature Selection and Model Optimization," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
  17. Manuel Sánchez-Montañés & Pablo Rodríguez-Belenguer & Antonio J. Serrano-López & Emilio Soria-Olivas & Yasser Alakhdar-Mohmara, 2020. "Machine Learning for Mortality Analysis in Patients with COVID-19," IJERPH, MDPI, vol. 17(22), pages 1-20, November.
  18. Elias Cavalcante-Filho & Flavio Abdenur, Rodrigo De Losso, 2018. "Machine learning applied to accounting variables yields the risk-return metrics of private company portfolios," Working Papers, Department of Economics 2018_23, University of São Paulo (FEA-USP).
  19. Pang, Junheng & Dong, Sheng, 2023. "A novel multivariable hybrid model to improve short and long-term significant wave height prediction," Applied Energy, Elsevier, vol. 351(C).
  20. Danying Du & Baozhong He & Xuefeng Luo & Shilong Ma & Yaning Song & Wen Yang, 2024. "Spatio-Temporal Variation Analysis of Soil Salinization in the Ougan-Kuqa River Oasis of China," Sustainability, MDPI, vol. 16(7), pages 1-22, March.
  21. Cooray, Upul & Watt, Richard G. & Tsakos, Georgios & Heilmann, Anja & Hariyama, Masanori & Yamamoto, Takafumi & Kuruppuarachchige, Isuruni & Kondo, Katsunori & Osaka, Ken & Aida, Jun, 2021. "Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis," Social Science & Medicine, Elsevier, vol. 291(C).
  22. Jamei, Mehdi & Ali, Mumtaz & Karbasi, Masoud & Xiang, Yong & Ahmadianfar, Iman & Yaseen, Zaher Mundher, 2022. "Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach," Applied Energy, Elsevier, vol. 326(C).
  23. Asma Shaheen & Javed Iqbal, 2018. "Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm," Sustainability, MDPI, vol. 10(3), pages 1-20, March.
  24. Nkiruka C. Atuegwu & Cheryl Oncken & Reinhard C. Laubenbacher & Mario F. Perez & Eric M. Mortensen, 2020. "Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach," IJERPH, MDPI, vol. 17(19), pages 1-16, October.
  25. Matthew James Grainger & Lusine Aramyan & Simone Piras & Thomas Edward Quested & Simone Righi & Marco Setti & Matteo Vittuari & Gavin Bruce Stewart, 2018. "Model selection and averaging in the assessment of the drivers of household food waste to reduce the probability of false positives," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-16, February.
  26. Jihong Qu & Kun Ren & Xiaoyu Shi, 2021. "Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 1029-1045, February.
  27. Simon Besnard & Nuno Carvalhais & M Altaf Arain & Andrew Black & Benjamin Brede & Nina Buchmann & Jiquan Chen & Jan G P W Clevers & Loïc P Dutrieux & Fabian Gans & Martin Herold & Martin Jung & Yoshik, 2019. "Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-22, February.
  28. Seung-Hun Lee & Hyeon-Seong Ju & Sang-Hun Lee & Sung-Woo Kim & Hun-Young Park & Seung-Wan Kang & Young-Eun Song & Kiwon Lim & Hoeryong Jung, 2021. "Estimation of Health-Related Physical Fitness (HRPF) Levels of the General Public Using Artificial Neural Network with the National Fitness Award (NFA) Datasets," IJERPH, MDPI, vol. 18(19), pages 1-13, October.
  29. Gordana Kaplan & Mateo Gašparović & Onur Kaplan & Vancho Adjiski & Resul Comert & Mohammad Asef Mobariz, 2023. "Machine Learning-Based Classification of Asbestos-Containing Roofs Using Airborne RGB and Thermal Imagery," Sustainability, MDPI, vol. 15(7), pages 1-16, March.
  30. Carolina Alves Costa Silva & Gianmarco Piccinno & Déborah Suissa & Mélanie Bourgin & Gerty Schreibelt & Sylvère Durand & Roxanne Birebent & Marine Fidelle & Cissé Sow & Fanny Aprahamian & Paolo Manghi, 2024. "Influence of microbiota-associated metabolic reprogramming on clinical outcome in patients with melanoma from the randomized adjuvant dendritic cell-based MIND-DC trial," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
  31. Ramón Ferri-García & María del Mar Rueda, 2022. "Variable selection in Propensity Score Adjustment to mitigate selection bias in online surveys," Statistical Papers, Springer, vol. 63(6), pages 1829-1881, December.
  32. Francesco Sartor & Jonathan P. Moore & Hans-Peter Kubis, 2021. "Plasma Interleukin-10 and Cholesterol Levels May Inform about Interdependences between Fitness and Fatness in Healthy Individuals," IJERPH, MDPI, vol. 18(4), pages 1-19, February.
  33. Tomasz Szul & Sylwester Tabor & Krzysztof Pancerz, 2021. "Application of the BORUTA Algorithm to Input Data Selection for a Model Based on Rough Set Theory (RST) to Prediction Energy Consumption for Building Heating," Energies, MDPI, vol. 14(10), pages 1-13, May.
  34. 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).
  35. Sašo Karakatič, 2020. "EvoPreprocess—Data Preprocessing Framework with Nature-Inspired Optimization Algorithms," Mathematics, MDPI, vol. 8(6), pages 1-29, June.
  36. Jiří Šandera & Přemysl Štych, 2020. "Selecting Relevant Biological Variables Derived from Sentinel-2 Data for Mapping Changes from Grassland to Arable Land Using Random Forest Classifier," Land, MDPI, vol. 9(11), pages 1-20, October.
  37. Maik Pietzner & Eleanor Wheeler & Julia Carrasco-Zanini & Nicola D. Kerrison & Erin Oerton & Mine Koprulu & Jian’an Luan & Aroon D. Hingorani & Steve A. Williams & Nicholas J. Wareham & Claudia Langen, 2021. "Synergistic insights into human health from aptamer- and antibody-based proteomic profiling," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
  38. Ramaharo, Franck Maminirina & RANDRIAMIFIDY, Michael Fitiavana, 2023. "Determinants of renewable energy consumption in Madagascar: Evidence from feature selection algorithms," AfricArxiv pfrhx, Center for Open Science.
  39. Iain S. Forrest & Ben O. Petrazzini & Áine Duffy & Joshua K. Park & Anya J. O’Neal & Daniel M. Jordan & Ghislain Rocheleau & Girish N. Nadkarni & Judy H. Cho & Ashira D. Blazer & Ron Do, 2023. "A machine learning model identifies patients in need of autoimmune disease testing using electronic health records," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  40. Ghosh, Indranil & Chaudhuri, Tamal Datta & Alfaro-Cortés, Esteban & Gámez, Matías & García, Noelia, 2022. "A hybrid approach to forecasting futures prices with simultaneous consideration of optimality in ensemble feature selection and advanced artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
  41. Kresova, Svetlana & Hess, Sebastian, 2021. "Determinants of Regional Raw Milk Prices in Russia," 61st Annual Conference, Berlin, Germany, September 22-24, 2021 317051, German Association of Agricultural Economists (GEWISOLA).
  42. Samuel Asante Gyamerah, 2019. "Are Bitcoins price predictable? Evidence from machine learning techniques using technical indicators," Papers 1909.01268, arXiv.org.
  43. Andrea Albergoni & Florentina J. Hettinga & Wim Stut & Francesco Sartor, 2020. "Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence," IJERPH, MDPI, vol. 17(17), pages 1-18, August.
  44. Alexander Kirpich & Elizabeth A Ainsworth & Jessica M Wedow & Jeremy R B Newman & George Michailidis & Lauren M McIntyre, 2018. "Variable selection in omics data: A practical evaluation of small sample sizes," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-19, June.
  45. Reza Arabi Belaghi & Joseph Beyene & Sarah D McDonald, 2021. "Prediction of preterm birth in nulliparous women using logistic regression and machine learning," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-22, June.
  46. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2020. "Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data," Working Papers 2020-04, Joint Research Centre, European Commission.
  47. Joseph, Lionel P. & Deo, Ravinesh C. & Prasad, Ramendra & Salcedo-Sanz, Sancho & Raj, Nawin & Soar, Jeffrey, 2023. "Near real-time wind speed forecast model with bidirectional LSTM networks," Renewable Energy, Elsevier, vol. 204(C), pages 39-58.
  48. Guillaume Belly & Lukas Boeckelmann & Carlos Mateo Caicedo Graciano & Alberto Di Iorio & Klodiana Istrefi & Vasileios Siakoulis & Arthur Stalla‐Bourdillon, 2023. "Forecasting sovereign risk in the Euro area via machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 657-684, April.
  49. Nawin Raj, 2022. "Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning," Mathematics, MDPI, vol. 10(23), pages 1-23, November.
  50. Svetlana Kresova & Sebastian Hess, 2022. "Identifying the Determinants of Regional Raw Milk Prices in Russia Using Machine Learning," Agriculture, MDPI, vol. 12(7), pages 1-18, July.
  51. Sara Saadatmand & Khodakaram Salimifard & Reza Mohammadi & Alex Kuiper & Maryam Marzban & Akram Farhadi, 2023. "Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients," Annals of Operations Research, Springer, vol. 328(1), pages 1043-1071, September.
  52. Tao, Jiawei & Dai, Hongyan & Chen, Weiwei & Jiang, Hai, 2023. "The value of personalized dispatch in O2O on-demand delivery services," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1022-1035.
  53. Tang, Rui & Yildiz, Baran & Leong, Philip H.W. & Vassallo, Anthony & Dore, Jonathon, 2019. "Residential battery sizing model using net meter energy data clustering," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
  54. Marc Deffland & Claudia Spies & Bjoern Weiss & Niklas Keller & Mirjam Jenny & Jochen Kruppa & Felix Balzer, 2020. "Effects of pain, sedation and delirium monitoring on clinical and economic outcome: A retrospective study," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-14, September.
  55. Raihan Rafif & Sandiaga Swahyu Kusuma & Siti Saringatin & Giara Iman Nanda & Pramaditya Wicaksono & Sanjiwana Arjasakusuma, 2021. "Crop Intensity Mapping Using Dynamic Time Warping and Machine Learning from Multi-Temporal PlanetScope Data," Land, MDPI, vol. 10(12), pages 1-18, December.
  56. Peter L. Watson & Marika Koukoula & Emmanouil Anagnostou, 2021. "Influence of the Characteristics of Weather Information in a Thunderstorm-Related Power Outage Prediction System," Forecasting, MDPI, vol. 3(3), pages 1-20, August.
  57. Yiping Peng & Ting Wang & Shujuan Xie & Zhenhua Liu & Chenjie Lin & Yueming Hu & Jianfang Wang & Xiaoyun Mao, 2023. "Estimation of Soil Cations Based on Visible and Near-Infrared Spectroscopy and Machine Learning," Agriculture, MDPI, vol. 13(6), pages 1-12, June.
  58. Park, Eunhye & Park, Jinah & Hu, Mingming, 2021. "Tourism demand forecasting with online news data mining," Annals of Tourism Research, Elsevier, vol. 90(C).
  59. Wei Pan & Jonathan Flint & Liat Shenhav & Tianli Liu & Mingming Liu & Bin Hu & Tingshao Zhu, 2019. "Re-examining the robustness of voice features in predicting depression: Compared with baseline of confounders," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-14, June.
  60. Bhaskar Tripathi & Rakesh Kumar Sharma, 2023. "Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1919-1945, December.
  61. Manuel S. González Canché, 2022. "Post-purchase Federal Financial Aid: How (in)Effective is the IRS’s Student Loan Interest Deduction (SLID) in Reaching Lower-Income Taxpayers and Students?," Research in Higher Education, Springer;Association for Institutional Research, vol. 63(6), pages 933-986, September.
  62. David Stephens & Markus Diesing, 2014. "A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-14, April.
  63. F. Michele & E. Stagnini & D. Pera & B. Rubino & R. Aloisio & A. Askan & P. Marcati, 2023. "Comparison of machine learning tools for damage classification: the case of L’Aquila 2009 earthquake," 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. 116(3), pages 3521-3546, April.
  64. Gang Chen & Xianju Li & Weitao Chen & Xinwen Cheng & Yujin Zhang & Shengwei Liu, 2014. "Extraction and application analysis of landslide influential factors based on LiDAR DEM: a case study in the Three Gorges area, China," 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. 74(2), pages 509-526, November.
  65. Baozhong He & Jianli Ding & Wenjiang Huang & Xu Ma, 2023. "Spatiotemporal Variation and Future Predictions of Soil Salinization in the Werigan–Kuqa River Delta Oasis of China," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
  66. Kian Tehranian, 2023. "Can Machine Learning Catch Economic Recessions Using Economic and Market Sentiments?," Papers 2308.16200, arXiv.org.
  67. Ewa Wilk & Małgorzata Krówczyńska & Bogdan Zagajewski, 2019. "Modelling the Spatial Distribution of Asbestos—Cement Products in Poland with the Use of the Random Forest Algorithm," Sustainability, MDPI, vol. 11(16), pages 1-13, August.
  68. Alex Gramegna & Paolo Giudici, 2022. "Shapley Feature Selection," FinTech, MDPI, vol. 1(1), pages 1-9, February.
  69. Carlos Família & Sarah R Dennison & Alexandre Quintas & David A Phoenix, 2015. "Prediction of Peptide and Protein Propensity for Amyloid Formation," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-16, August.
  70. Aivett Bilbao & Nathalie Munoz & Joonhoon Kim & Daniel J. Orton & Yuqian Gao & Kunal Poorey & Kyle R. Pomraning & Karl Weitz & Meagan Burnet & Carrie D. Nicora & Rosemarie Wilton & Shuang Deng & Ziyu , 2023. "PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
  71. Ghosh, Indranil & Datta Chaudhuri, Tamal & Alfaro-Cortés, Esteban & Gámez Martínez, Matías & García Rubio, Noelia, 2021. "Estimating the relative effects of raw material prices, sectoral outlook and market sentiment on stock prices," Resources Policy, Elsevier, vol. 73(C).
  72. Samuel José Silva Soares da Rocha & Flora Magdaline Benitez Romero & Carlos Moreira Miquelino Eleto Torres & Laércio Antônio Gonçalves Jacovine & Sabina Cerruto Ribeiro & Paulo Henrique Villanova & Br, 2023. "Machine Learning: Volume and Biomass Estimates of Commercial Trees in the Amazon Forest," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
  73. Kresova, Svetlana & Hess, Sebastian, 2021. "Determinants of Regional Raw Milk Prices in Russia," 2021 Conference, August 17-31, 2021, Virtual 315064, International Association of Agricultural Economists.
  74. 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.
  75. Faisal Alsayegh & Moh A Alkhamis & Fatima Ali & Sreeja Attur & Nicholas M Fountain-Jones & Mohammad Zubaid, 2022. "Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-15, January.
  76. Dong-Hee Cho & Seung-Hyun Moon & Yong-Hyuk Kim, 2021. "Genetic Feature Selection Applied to KOSPI and Cryptocurrency Price Prediction," Mathematics, MDPI, vol. 9(20), pages 1-19, October.
  77. Jaewon Park & Minsoo Shin, 2022. "An Approach for Variable Selection and Prediction Model for Estimating the Risk-Based Capital (RBC) Based on Machine Learning Algorithms," Risks, MDPI, vol. 10(1), pages 1-20, January.
  78. Sangjin Kim & Jong-Min Kim, 2019. "Two-Stage Classification with SIS Using a New Filter Ranking Method in High Throughput Data," Mathematics, MDPI, vol. 7(6), pages 1-16, May.
  79. Patrick Kenekayoro, 2018. "Identifying named entities in academic biographies with supervised learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 751-765, August.
  80. Chrats Melkonian & Francisco Zorrilla & Inge Kjærbølling & Sonja Blasche & Daniel Machado & Mette Junge & Kim Ib Sørensen & Lene Tranberg Andersen & Kiran R. Patil & Ahmad A. Zeidan, 2023. "Microbial interactions shape cheese flavour formation," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
  81. Efrat Muller & Itamar Shiryan & Elhanan Borenstein, 2024. "Multi-omic integration of microbiome data for identifying disease-associated modules," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  82. Nanna Munck & Patrick Murigu Kamau Njage & Pimlapas Leekitcharoenphon & Eva Litrup & Tine Hald, 2020. "Application of Whole‐Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1693-1705, September.
  83. Tanzeela Khalid & Raphael Aggio & Paul White & Ben De Lacy Costello & Raj Persad & Huda Al-Kateb & Peter Jones & Chris S Probert & Norman Ratcliffe, 2015. "Urinary Volatile Organic Compounds for the Detection of Prostate Cancer," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-15, November.
  84. Aras, Serkan & Hanifi Van, M., 2022. "An interpretable forecasting framework for energy consumption and CO2 emissions," Applied Energy, Elsevier, vol. 328(C).
  85. Fabian Schäfer & Manuel Walther & Dominik G. Grimm & Alexander Hübner, 2023. "Combining machine learning and optimization for the operational patient-bed assignment problem," Health Care Management Science, Springer, vol. 26(4), pages 785-806, December.
  86. Arjan S. Gosal & Janine A. McMahon & Katharine M. Bowgen & Catherine H. Hoppe & Guy Ziv, 2021. "Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness," Land, MDPI, vol. 10(6), pages 1-14, May.
  87. Piotr Pomorski & Denise Gorse, 2023. "Improving Portfolio Performance Using a Novel Method for Predicting Financial Regimes," Papers 2310.04536, arXiv.org.
  88. Gehan A. Mousa & Elsayed A. H. Elamir & Khaled Hussainey, 2022. "Using machine learning methods to predict financial performance: Does disclosure tone matter?," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 19(1), pages 93-112, March.
  89. Vito Janko & Gašper Slapničar & Erik Dovgan & Nina Reščič & Tine Kolenik & Martin Gjoreski & Maj Smerkol & Matjaž Gams & Mitja Luštrek, 2021. "Machine Learning for Analyzing Non-Countermeasure Factors Affecting Early Spread of COVID-19," IJERPH, MDPI, vol. 18(13), pages 1-33, June.
  90. 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.
  91. Ali Asghar Heidari & Mehdi Akhoondzadeh & Huiling Chen, 2022. "A Wavelet PM2.5 Prediction System Using Optimized Kernel Extreme Learning with Boruta-XGBoost Feature Selection," Mathematics, MDPI, vol. 10(19), pages 1-35, September.
  92. Jaewon Park & Minsoo Shin & Wookjae Heo, 2021. "Estimating the BIS Capital Adequacy Ratio for Korean Banks Using Machine Learning: Predicting by Variable Selection Using Random Forest Algorithms," Risks, MDPI, vol. 9(2), pages 1-19, February.
  93. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2022. "A babel of web-searches: Googling unemployment during the pandemic," Labour Economics, Elsevier, vol. 74(C).
  94. Mao, Xiaojun & Peng, Liuhua & Wang, Zhonglei, 2022. "Nonparametric feature selection by random forests and deep neural networks," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
  95. Ludmila Marcinowicz & Andrei Shpakou & Siarhei Piatrou & Ewa Fejfer‐Wirbal & Agnieszka Dudzik & Paulina Kalinowska & Sviatlana Palubinskaya & Danuta Wojnar, 2020. "Behavioural categories of professionalism of nurses in Poland and Belarus: A comparative survey," Journal of Clinical Nursing, John Wiley & Sons, vol. 29(9-10), pages 1635-1642, May.
  96. Moreno Badia, Marialuz & Medas, Paulo & Gupta, Pranav & Xiang, Yuan, 2022. "Debt is not free," Journal of International Money and Finance, Elsevier, vol. 127(C).
  97. Zhao-Yue Chen & Hervé Petetin & Raúl Fernando Méndez Turrubiates & Hicham Achebak & Carlos Pérez García-Pando & Joan Ballester, 2024. "Population exposure to multiple air pollutants and its compound episodes in Europe," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  98. Virginia Puyana-Romero & Angela María Díaz-Márquez & Giuseppe Ciaburro & Ricardo Hernández-Molina, 2022. "The Acoustic Environment and University Students’ Satisfaction with the Online Education Method during the COVID-19 Lockdown," IJERPH, MDPI, vol. 20(1), pages 1-27, December.
  99. Jorgen A Wullems & Sabine M P Verschueren & Hans Degens & Christopher I Morse & Gladys L Onambélé, 2017. "Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-18, November.
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