Random Forests

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Items 301–400 of 1,834

1. MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites
   Esteban, Oscar, Birman, Daniel, Schaer, Marie, Koyejo, Oluwasanmi O., Poldrack, Russell A., Gorgolewski, Krzysztof J. · 2017 · PLOS ONE
2. Human Tumor-Associated Macrophage and Monocyte Transcriptional Landscapes Reveal Cancer-Specific Reprogramming, Biomarkers, and Therapeutic Targets
   Luca Cassetta, Stamatina Fragkogianni, Andrew H. Sims, Agnieszka Swierczak, Lesley M. Forrester, Hui Zhang, Daniel Y. H. Soong, Tiziana Cotechini, Pavana Anur, Elaine Y. Lin et al. · 2019 · Cancer Cell
3. Stress Physiology of Lactic Acid Bacteria.
   Papadimitriou, Konstantinos, Alegría, Ángel, Bron, Peter A., de Angelis, Maria, Gobbetti, Marco, Kleerebezem, Michiel, Lemos, José A., Linares, Daniel M., Ross, Paul, Stanton, Catherine et al. · 2016 · Microbiology and Molecular Biology Reviews
4. A tutorial review: Metabolomics and partial least squares-discriminant analysis – a marriage of convenience or a shotgun wedding
   Piotr S. Gromski, Howbeer Muhamadali, David I. Ellis, Yun Xu A, Elon Santos Correa, Michael Lewis Turner, Royston Goodacre · 2015 · Analytica Chimica Acta
5. QSAR without borders
   Muratov, Eugene N., Bajorath, Jürgen, Sheridan, Robert P., Tetko, Igor V., Filimonov, Dmitry, Poroikov, Vladimir, Oprea, Tudor I., Baskin, Igor I., Varnek, Alexandre, Roitberg, Adrian et al. · 2020 · Chemical Society Reviews
6. Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization
   Wengang Zhang, Chongzhi Wu, Haiyi Zhong, Yongqin Li, Lin Wang · 2020 · Geoscience Frontiers
7. Cross-National Analysis of the Associations among Mental Disorders and Suicidal Behavior: Findings from the WHO World Mental Health Surveys
   Nock, Matthew K., Hwang, Irving, Sampson, Nancy, Kessler, Ronald C., Angermeyer, Matthias, Beautrais, Annette, Borges, Guilherme, Bromet, Evelyn, Bruffaerts, Ronny, de Girolamo, Giovanni et al. · 2009 · PLoS Medicine
8. A survey of multiple classifier systems as hybrid systems
   Michał Woźniak, Manuel Graña, Emilio Corchado · 2013 · Information Fusion
9. ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database
   Jie Nan Dong, Ningning Wang, Zhi‐Jiang Yao, Lin Zhang, Yan Cheng, Defang Ouyang, Aiping Lü, Dongsheng Cao · 2018 · Journal of Cheminformatics
10. Tropical forests are a net carbon source based on aboveground measurements of gain and loss
    Baccini, A., Walker, W., Carvalho, L., Farina, M., Sulla-Menashe, D., Houghton, R. A. · 2017 · Science
11. Using machine learning approaches for multi-omics data analysis: A review
    Reel, Parminder S., Reel, Smarti, Pearson, Ewan, Trucco, Emanuele, Jefferson, Emily · 2021 · Biotechnology Advances
12. Predictive compound accumulation rules yield a broad-spectrum antibiotic
    Michelle F. Richter, Bryon Drown, Andrew P. Riley, Alfredo Garcia, Tomohiro Shirai, Riley L. Svec, Paul J. Hergenrother · 2017 · Nature
13. Boruta – A System for Feature Selection
    Miron B. Kursa, Aleksander Jankowski, Witold Remigiusz Rudnicki · 2010 · Fundamenta Informaticae
14. RFMix: A Discriminative Modeling Approach for Rapid and Robust Local-Ancestry Inference
    Maples, Brian K., Gravel, Simon, Kenny, Eimear E., Bustamante, Carlos D. · 2013 · The American Journal of Human Genetics
15. The role of multiple global change factors in driving soil functions and microbial biodiversity
    Matthias C. Rillig, Masahiro Ryo, Anika Lehmann, Carlos A. Aguilar‐Trigueros, Sabine Buchert, Anja Wulf, Aiko Iwasaki, Julien Roy, Gaowen Yang · 2019 · Science
16. Efficient and Robust Feature Extraction by Maximum Margin Criterion
    H. Li, Tao Jiang, Kai Zhang · 2006 · IEEE Transactions on Neural Networks
17. Power of data in quantum machine learning
    Huang, Hsin-Yuan, Broughton, Michael, Mohseni, Masoud, Babbush, Ryan, Boixo, Sergio, Neven, Hartmut, McClean, Jarrod R. · 2021 · Nature Communications
18. Random forest versus logistic regression: a large-scale benchmark experiment
    Raphaël Couronné, Philipp Probst, Anne‐Laure Boulesteix · 2018 · BMC Bioinformatics
19. Dynamic population mapping using mobile phone data
    Pierre Deville, Catherine Linard, Samuel Martin, Marius Gilbert, Forrest Robert Stevens, Andrea Elizabeth Gaughan, Vincent D. Blondel, Andrew J. Tatem · 2014 · Proceedings of the National Academy of Sciences
20. Genotyping‐by‐Sequencing for Plant Breeding and Genetics
    Poland, Jesse A., Rife, Trevor W. · 2012 · The Plant Genome
21. Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
    Omid Ghorbanzadeh, Thomas Blaschke, Khalil Gholamnia, Sansar Raj Meena, Dirk Tiede A, Jagannath Aryal · 2019 · Remote Sensing
22. The effects of climate extremes on global agricultural yields
    Elisabeth Vogel, Markus G. Donat, Lisa V. Alexander, Malte Alexander Meinshausen, Deepak K. Ray, David John Karoly, Nicolai Meinshausen, Katja Frieler · 2019 · Environmental Research Letters
23. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines
    Paul Geeleher, Nancy Jean Cox, R. Stephanie Huang · 2014 · Genome biology
24. Improving propensity score weighting using machine learning
    Brian K. Lee, Justin Lessler, Elizabeth A. Stuart · 2009 · Statistics in Medicine
25. Changes in plant community composition lag behind climate warming in lowland forests
    Romain Bertrand, Jonathan Lenoir, Christian Piedallu, Gabriela Riofrío‐Dillon, Patrice de Ruffray, Claude Vidal, J. C. Pierrat, Jean‐Claude Gégout · 2011 · Nature
26. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms
    Gianluca Tramontana, Martin Jung, Christopher R. Schwalm, Kazuhito Ichii, Gustau Camps‐Valls, Botond Ráduly, Markus Reichstein, Muhammad Altaf Arain, Alessandro Cescatti, Gerard Kiely et al. · 2016 · Biogeosciences
27. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems
    Dengsheng Lu, Qi Chen, Guangxing Wang, Lijuan Liu, Guiying Li, Emilio Federico Moran · 2014 · International Journal of Digital Earth
28. American Gut: an Open Platform for Citizen-Science Microbiome Research
    McDonalda, Daniel, Hyde, Embriette, Debelius, Justine W., Morton, James T., Gonzalez, Antonio, Ackermann, Gail, Aksenov, Alexander A., Behsaz, Bahar, Brennan, Caitriona, Chen, Yingfeng et al. · 2018
29. Supervised Machine Learning: A Brief Primer
    Tammy Jiang, Jaimie L. Gradus, Anthony Joseph Rosellini · 2020 · Behavior Therapy
30. Brain atrophy in Alzheimer’s Disease and aging
    Lorenzo Pini, Michela Pievani, Martina Bocchetta, Daniele Altomare, Paolo Bosco, Enrica Cavedo, Samantha Galluzzi, Moira Marizzoni, Giovanni Battista Frisoni · 2016 · Ageing Research Reviews
31. Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy
    Warwick B. Dunn, David Broadhurst, Helen J. Atherton, Royston Goodacre, Julian Leether Griffin · 2010 · Chemical Society Reviews
32. Machine learning in bioinformatics
    Larrañaga, Pedro, Calvo, Borja, Santana, Roberto, Bielza, Concha, Galdiano, Josu, Inza, Iñaki, Lozano, José A., Armañanzas, Rubén, Santafé, Guzmán, Pérez, Aritz et al. · 2006 · Briefings in Bioinformatics
33. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm
    Onisimo Mutanga, Elhadi M. I. Adam, Moses Azong Cho · 2012 · International Journal of Applied Earth Observation and Geoinformation
34. Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery
    Jonathan Cheung-Wai Chan, Desiré Paelinckx · 2008 · Remote Sensing of Environment
35. An expansion of rare lineage intestinal microbes characterizes rheumatoid arthritis
    Chen, Jun, Wright, Kerry, Davis, John M., Jeraldo, Patricio, Marietta, Eric V., Murray, Joseph, Nelson, Heidi, Matteson, Eric L., Taneja, Veena · 2016 · Genome Medicine
36. Tree-Based Batch Mode Reinforcement Learning
    Damien Ernst, Pierre Geurts, Louis A. Wehenkel · 2005
37. From DFT to machine learning: recent approaches to materials science–a review
    Schleder, Gabriel R, Padilha, Antonio C M, Acosta, Carlos Mera, Costa, Marcio, Fazzio, Adalberto · 2019 · Journal of Physics: Materials
38. Face Alignment at 3000 FPS via Regressing Local Binary Features
    Shaoqing Ren, Xudong Cao, Yichen Wei, Jian Zhong Sun · 2014 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
39. PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations
    Jaroslav M. Bendl, Jan Štourač, Ondřej Šalanda, Antonín Pavelka, Eric D. Wieben, Jaroslav Zendulka, Jan Brezovský, Jiřı́ Damborský · 2014 · PLoS Computational Biology
40. Deep forest
    Zhi‐Hua Zhou, Ji Feng · 2018 · National Science Review
41. Random Forest for Bioinformatics
    Yanjun Qi · 2012 · Springer eBooks
42. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling
    Jason N. Goetz, Alexander Brenning, Helene Petschko, P. Leopold · 2015 · Computers & Geosciences
43. A gradient boosting method to improve travel time prediction
    Yanru Zhang, Ali E. Haghani · 2015 · Transportation Research Part C Emerging Technologies
44. Explainable artificial intelligence: an analytical review
    Angelov, Plamen P., Soares, Eduardo A., Jiang, Richard, Arnold, Nicholas I., Atkinson, Peter M. · 2021 · WIREs Data Mining and Knowledge Discovery
45. Understanding variable importances in forests of randomized trees
    Gilles Louppe, Louis A. Wehenkel, Antonio Sutera, Pierre Geurts · 2013 · ORBi (University of Liège)
46. ADMET modeling approaches in drug discovery
    Leonardo L. G. Ferreira, Adriano Defini Andricopulo · 2019 · Drug Discovery Today
47. Meta‐analyses of human gut microbes associated with obesity and IBD
    Walters, William A., Xu, Zech, Knight, Rob · 2014 · FEBS Letters
48. Adaptive random forests for evolving data stream classification
    Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfharinger, Geoffrey Holmes, Talel Abdessalem · 2017 · Machine Learning
49. An Introduction to Machine Learning
    Solveig Badillo, Balázs Bánfai, Fabian Birzele, Iakov I. Davydov, Lucy G. Hutchinson, Tony Kam‐Thong, Juliane Siebourg‐Polster, Bernhard Steiert, Jitao David Zhang · 2020 · Clinical Pharmacology & Therapeutics
50. Extending the Linear Model with R
    Julian J. Faraway · 2016 · Chapman & Hall/CRC eBooks
51. SEGCloud: Semantic Segmentation of 3D Point Clouds
    Lyne P. Tchapmi, Christopher Choy, Iro Armeni, JunYoung Gwak, Silvio Savarese · 2017 · International Conference on 3D Vision (3DV)
52. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review
    Jochem Verrelst, Gustau Camps‐Valls, Jordi Muñoz-Marı́, Juan Pablo Rivera-Caicedo, Frank Veroustraete, Jan G.P.W. Clevers, J. Moreno · 2015 · ISPRS Journal of Photogrammetry and Remote Sensing
53. Modeling and forecasting building energy consumption: A review of data-driven techniques
    Mathieu Bourdeau, Xiao qiang Zhai, Elyes Nefzaoui, Xiaofeng Guo, Patrice Chatellier · 2019 · Sustainable Cities and Society
54. Flood hazard risk assessment model based on random forest
    Zhaoli Wang, Chengguang Lai, Xiaohong Chen, Bing Kuan Yang, Shiwei Zhao, Xiaoyan Bai · 2015 · Journal of Hydrology
55. Feature Selection for Classification of Hyperspectral Data by SVM
    Mahesh Pal, Giles M. Foody · 2010 · IEEE Transactions on Geoscience and Remote Sensing
56. Automated inference of molecular mechanisms of disease from amino acid substitutions
    Biao Li, Vidhya Gomathi Krishnan, Matthew E. Mort, Fuxiao Xin, Kishore K. Kamati, D.N. Cooper, Sean David Mooney, Predrag Radivojac A · 2009 · Bioinformatics
57. Sample size for binary logistic prediction models: Beyond events per variable criteria
    M. van Smeden, K. Moons, J. D. de Groot, G. Collins, D. Altman, M. Eijkemans, J. Reitsma · 2018 · Statistical Methods in Medical Research
58. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation
    Zhang, Wenlu, Li, Rongjian, Deng, Houtao, Wang, Li, Lin, Weili, Ji, Shuiwang, Shen, Dinggang · 2015 · NeuroImage
59. Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis
    John W. Goodell, Satish Kumar, Weng Marc Lim, Debidutta Pattnaik · 2021 · Journal of Behavioral and Experimental Finance
60. Discovery of a Cytokine and Its Receptor by Functional Screening of the Extracellular Proteome
    Haishan Lin, Ernestine Lee, Kevin Hestir, Cindy Leo, Minmei Huang, Elizabeth Bosch, Robert Halenbeck, Ge Wu, Aileen J. Zhou, Dirk Behrens et al. · 2008 · Science
61. Emerging signals of declining forest resilience under climate change
    Giovanni Forzieri, Vasilis Dakos, Nate G. McDowell, Ramdane Alkama, Alessandro Cescatti · 2022 · Nature
62. HCP: A Flexible CNN Framework for Multi-Label Image Classification
    Yunchao Wei, Wei Xia, Min Lin, Junshi Huang, Bingbing Ni, Jian Dong, Yao Zhao, Shuicheng Yan · 2015 · IEEE Transactions on Pattern Analysis and Machine Intelligence
63. Ensemble Learning
    Robi Polikar A · 2012 · Springer eBooks
64. Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study
    Anoop D Shah, Jonathan William Bartlett, James Robert Carpenter, Owen Nicholas, Harry Hemingway · 2014 · American Journal of Epidemiology
65. Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw
    Gustaf Hugelius, Julie Loisel, Sarah Chadburn, Robert B. Jackson, Miriam C. Jones, Glen M. Sproul Dit MacDonald, Maija E. Marushchak, David Olefeldt, Maara S. Packalen, Matthias Siewert et al. · 2020 · Proceedings of the National Academy of Sciences
66. Models, forests, and trees of York English: Was/were variation as a case study for statistical practice
    Sali A. Tagliamonte, R. Harald Baayen · 2012 · Language Variation and Change
67. Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
    Zhang, Jianhua, Yin, Zhong, Chen, Peng, Nichele, Stefano · 2020 · Information Fusion
68. What Is Machine Learning?
    Issam El Naqa, Martin J. Murphy · 2015 · Springer eBooks
69. Machine-learning approaches in drug discovery: methods and applications
    Antonio Lavecchia · 2014 · Drug Discovery Today
70. Exosome Transfer from Stromal to Breast Cancer Cells Regulates Therapy Resistance Pathways
    M. Boelens, Tony J. Wu, B. Nabet, Bi-Hui Xu, Yu Qiu, T. Yoon, Diana J Azzam, Christina Twyman-Saint Victor, B. Wiemann, H. Ishwaran et al. · 2014 · Cell
71. Machine Learning for Survival Analysis
    Ping Wang, Yan Li, Chandan K. Reddy · 2019 · ACM Computing Surveys
72. Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools
    Athanasios Tsanas, Angeliki Xifara · 2012 · Energy and Buildings
73. Random forest missing data algorithms
    Fei Tang, Hemant Ishwaran · 2017 · Statistical Analysis and Data Mining The ASA Data Science Journal
74. The Human Gene Mutation Database (HGMD®): optimizing its use in a clinical diagnostic or research setting
    Peter D. Stenson, Matthew E. Mort, Edward V. Ball, Molly Chapman, Katy Evans, Luı́sa Azevedo, Matthew Hayden, Sally Heywood, David S. Millar, Andrew D. Phillips et al. · 2020 · Human Genetics
75. Unexpected Abrupt Increase in Left Ventricular Assist Device Thrombosis
    Randall C. Starling, Nader Moazami, Scott Silvestry, Gregory Alan Ewald, Joseph G. Rogers, Carmelo A. Milano, Jesus Eduardo Rame, Michael A. Acker, Eugene H. Blackstone, John Ehrlinger et al. · 2013 · New England Journal of Medicine
76. A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources
    Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis · 2019 · Water
77. Detecting spammers on social networks
    Stringhini, Gianluca, Kruegel, Christopher, Vigna, Giovanni · 2010 · Proceedings of the 26th Annual Computer Security Applications Conference
78. Accumulation of driver and passenger mutations during tumor progression
    Ivana Božić, Tibor Antal A, Hisashi Ohtsuki, Hannah K. Carter, Dewey Kim, Sining Chen, Rachel Karchin, Kenneth W. Kinzler, Bert Vogelstein, Martin A. Nowak · 2010 · Proceedings of the National Academy of Sciences
79. Random forests: from early developments to recent advancements
    Khaled Fawagreh, Mohamed Medhat Gaber, Eyad Elyan · 2014 · Systems Science & Control Engineering
80. A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring
    Yufei Xia, Chuanzhe Liu, Yuying Li, Nana Liu · 2017 · Expert Systems with Applications
81. Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product
    Damien Sulla‐Menashe, Josh Gray, Stewart P. Abercrombie, M. A. Friedl · 2019 · Remote Sensing of Environment
82. Experimental perspectives on learning from imbalanced data
    Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano · 2007 · International Conference on Machine Learning
83. From Predictive to Prescriptive Analytics
    Bertsimas, Dimitris, Kallus, Nathan · 2020 · Management Science
84. A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer
    Johanna Wagner, Marianna Rapsomaniki, Stéphane Chevrier, Tobias Anzeneder, Claus Langwieder, August Dykgers, Martin Rees, Annette Ramaswamy, Simone E. Muenst, Savas Deniz Soysal et al. · 2019 · Cell
85. Detecting texts of arbitrary orientations in natural images
    Cong Yao, Xiang Bai, Wenyu Liu, Yi Ma, Zhuowen Tu · 2012 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
86. Deep neural network based malware detection using two dimensional binary program features
    Joshua Saxe, Konstantin Berlin · 2015 · International Conference on Malicious and Unwanted Software
87. Lidar sampling for large-area forest characterization: A review
    Michael A. Wulder, Joanne C. White, ROSS F. NELSON, Erik Næsset, Hans Ole Ørka, Nicholas C. Coops, Thomas Hilker, Christopher W. Bater, Terje Gobakken · 2012 · Remote Sensing of Environment
88. An Introduction to Topological Data Analysis: Fundamental and Practical Aspects for Data Scientists
    Frédéric Chazal, Bertrand Michel · 2021 · Frontiers in Artificial Intelligence
89. A survey on addressing high-class imbalance in big data
    Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya · 2018 · Journal Of Big Data
90. Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence
    Sebastian Raschka, Joshua M. Patterson, Corey Nolet · 2020 · Information
91. Factor analysis applied to regional geochemical data: problems and possibilities
    Clemens Reimann C, Peter Filzmoser A, R G Garrett · 2002 · Applied Geochemistry
92. What limits fire? An examination of drivers of burnt area in Southern Africa
    ARCHIBALD, SALLY, ROY, DAVID P., Van WILGEN, BRIAN W., SCHOLES, ROBERT J. · 2009 · Global Change Biology
93. The support vector machine under test
    DAVID A. MEYER, Friedrich Leisch, Kurt Hornik · 2003 · Neurocomputing
94. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia
    Ahmed M. Youssef, Hamid Reza Pourghasemi, Zohre Sadat Pourtaghi, Mohamed M. Al-Katheri · 2015 · Landslides
95. Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions
    De Sousa E Melo, Felipe, Wang, Xin, Jansen, Marnix, Fessler, Evelyn, Trinh, Anne, de Rooij, Laura P M H, de Jong, Joan H, de Boer, Onno J, van Leersum, Ronald, Bijlsma, Maarten F et al. · 2013 · Nature Medicine
96. Model-Based Recursive Partitioning
    Achim Zeileis, Torsten Hothorn, Kurt Hornik · 2008 · Journal of Computational and Graphical Statistics
97. An experimental comparison of classification algorithms for imbalanced credit scoring data sets
    Iain Brown, Christophe Mues · 2011 · Expert Systems with Applications
98. Cybersecurity data science: an overview from machine learning perspective
    Sarker, Iqbal H., Kayes, A. S. M., Badsha, Shahriar, Alqahtani, Hamed, Watters, Paul, Ng, Alex · 2020 · Journal of Big Data
99. Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
    Letham, Benjamin, Rudin, Cynthia, McCormick, Tyler H., Madigan, David · 2015 · The Annals of Applied Statistics
100. Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy
     Andrea Dal Pozzolo, Giacomo Boracchi, Olivier Caelen, Cesare Alippi, Gianluca Bontempi · 2017 · IEEE Transactions on Neural Networks and Learning Systems