Random Forests

Cited by (1,834)

Items 101–200 of 1,834

1. A survey on ensemble learning
   Dong, Xibin, Yu, Zhiwen, Cao, Wenming, Shi, Yifan, Ma, Qianli · 2020 · Frontiers of Computer Science
2. Where is positional uncertainty a problem for species distribution modelling?
   Babak Naimi, Nicholas Hamm, T.A. Groen, Andrew K. Skidmore, Albertus G. Toxopeus · 2013 · Ecography
3. Untitled work
   2010 · 10.5555/1756006.1859921
4. Analyzing Learned Molecular Representations for Property Prediction
   Yang, Kevin, Swanson, Kyle, Jin, Wengong, Coley, Connor, Eiden, Philipp, Gao, Hua, Guzman-Perez, Angel, Hopper, Timothy, Kelley, Brian, Mathea, Miriam et al. · 2019 · Journal of Chemical Information and Modeling
5. Global conservation outcomes depend on marine protected areas with five key features
   Edgar, Graham J., Stuart-Smith, Rick D., Willis, Trevor J., Kininmonth, Stuart, Baker, Susan C., Banks, Stuart, Barrett, Neville S., Becerro, Mikel A., Bernard, Anthony T. F., Berkhout, Just et al. · 2014 · Nature
6. CatBoost for big data: an interdisciplinary review
   John Hancock, Taghi M. Khoshgoftaar · 2020 · Journal Of Big Data
7. Multi-class AdaBoost
   Trevor J. Hastie, Saharon Rosset, Ji Zhu, Hui Zou · 2009 · Statistics and Its Interface
8. Rotation Forest: A New Classifier Ensemble Method
   Juan J. Rodríguez, Ludmila Ilieva Kuncheva, Carlos J. Alonso · 2006 · IEEE Transactions on Pattern Analysis and Machine Intelligence
9. A Dataset for Breast Cancer Histopathological Image Classification
   Fábio Alexandre Spanhol, Luiz S. Oliveira, Caroline Petitjean, Laurent Heutte · 2015 · IEEE Transactions on Biomedical Engineering
10. MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data
    Achal Dhariwal, Jasmine Chong, Salam M. Habib, Irah L. King, Luis B. Agellon, Jianguo Xia · 2017 · Nucleic Acids Research
11. BART: Bayesian additive regression trees
    Hugh A. Chipman, Edward I. George, Robert E. McCulloch · 2010 · The Annals of Applied Statistics
12. Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation
    Goldstein, Alex, Kapelner, Adam, Bleich, Justin, Pitkin, Emil · 2015 · Journal of Computational and Graphical Statistics
13. Continuous change detection and classification of land cover using all available Landsat data
    Zhe Zhu, Curtis E. Woodcock · 2014 · Remote Sensing of Environment
14. Bayesian community-wide culture-independent microbial source tracking
    Dan Knights, Justin Kuczynski, Emily Sarah Charlson, Jesse Zaneveld, Michael C. Mozer, Ronald G. Collman, Frederic D. Bushman, Rob D. Knight, Scott Theodore Kelley · 2011 · Nature Methods
15. Machine learning methods for solar radiation forecasting: A review
    Voyant, Cyril, Notton, Gilles, Kalogirou, Soteris, Nivet, Marie-Laure, Paoli, Christophe, Motte, Fabrice, Fouilloy, Alexis · 2017 · Renewable Energy
16. Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
    Hüllermeier, Eyke, Waegeman, Willem · 2021 · Machine Learning
17. A general-purpose machine learning framework for predicting properties of inorganic materials
    Ward, Logan, Agrawal, Ankit, Choudhary, Alok, Wolverton, Christopher · 2016 · npj Computational Materials
18. Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures
    Jennifer F. Bobb, Linda Valeri, Birgit Claus Henn, David C. Christiani, Robert O. Wright, Maitreyi Mazumdar, John J. Godleski, Brent A. Coull · 2014 · Biostatistics
19. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps
    Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P. S. A., Dubayah, R., Friedl, M. A. et al. · 2012 · Nature Climate Change
20. Classification in the Presence of Label Noise: A Survey
    Frenay, Benoit, Verleysen, Michel · 2014 · IEEE Transactions on Neural Networks and Learning Systems
21. Introduction to Radiomics
    Mayerhoefer, Marius E., Materka, Andrzej, Langs, Georg, Häggström, Ida, Szczypiński, Piotr, Gibbs, Peter, Cook, Gary · 2020 · Journal of Nuclear Medicine
22. Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers
    Salimi-Khorshidi, Gholamreza, Douaud, Gwenaëlle, Beckmann, Christian F., Glasser, Matthew F., Griffanti, Ludovica, Smith, Stephen M. · 2014 · NeuroImage
23. A gene-based association method for mapping traits using reference transcriptome data
    Eric R. Gamazon, Heather E Wheeler, Kaanan P. Shah, Sahar V. Mozaffari, Keston Aquino-Michaels, Robert J. Carroll, Anne E. Eyler, Joshua C. Denny, Dan L. Nicolae, Nancy Jean Cox et al. · 2015 · Nature Genetics
24. Graph-based approach for airborne light detection and ranging segmentation
    D.L. Vilariño, Jose Carlos Cabaleiro, Jorge Martínez Sánchez, Francisco F. Rivera, Tomás F. Pena · 2017 · Journal of Applied Remote Sensing
25. Measuring ecological niche overlap from occurrence and spatial environmental data
    Olivier Broennimann, Matthew C. Fitzpatrick, Peter B. Pearman, Blaise Petitpierre, Loïc Pellissier, Nigel G. Yoccoz, Wilfried Thuiller, Marie‐Josée Fortin, Christophe F. Randin, Niklaus E. Zimmermann et al. · 2011 · Global Ecology and Biogeography
26. Challenges in Representation Learning: A Report on Three Machine Learning Contests
    Goodfellow, Ian J., Erhan, Dumitru, Carrier, Pierre Luc, Courville, Aaron, Mirza, Mehdi, Hamner, Ben, Cukierski, Will, Tang, Yichuan, Thaler, David, Lee, Dong-Hyun et al. · 2013 · Lecture Notes in Computer Science
27. CLASSIFICATION OF IMBALANCED DATA: A REVIEW
    Yanmin Sun, Andrew K. C. Wong, Mohamed S. Kamel · 2009 · International Journal of Pattern Recognition and Artificial Intelligence
28. Deep patient: An unsupervised representation to predict the future of patients from the electronic health records
    Miotto, Riccardo, Li, Li, Kidd, Brian A., Dudley, Joel T. · 2016 · Scientific Reports
29. Comparing different supervised machine learning algorithms for disease prediction
    Shahadat Uddin, Arif M. Khan, Md Ekramul Hossain, Mohammad Ali Moni · 2019 · BMC Medical Informatics and Decision Making
30. Probability Estimates for Multi-class Classification by Pairwise Coupling
    Tingfan Wu, Chih‐Jen Lin, Ruby C. Weng · 2004 · Journal of Machine Learning Research
31. Machine learning for medical imaging
    Erickson, Bradley J., Korfiatis, Panagiotis, Akkus, Zeynettin, Kline, Timothy L. · 2017 · RadioGraphics
32. Variable selection – A review and recommendations for the practicing statistician
    Georg Heinze, Christine Wallisch, Daniela Dunkler · 2018 · Biometrical Journal
33. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data
    Jasmine Chong, Peng Liu, Guangyan Zhou, Jianguo Xia · 2020 · Nature Protocols
34. T-cell invigoration to tumour burden ratio associated with anti-PD-1 response
    Huang, Alexander C., Postow, Michael A., Orlowski, Robert J., Mick, Rosemarie, Bengsch, Bertram, Manne, Sasikanth, Xu, Wei, Harmon, Shannon, Giles, Josephine R., Wenz, Brandon et al. · 2017 · Nature
35. Phenotype molding of stromal cells in the lung tumor microenvironment
    Lambrechts, Diether, Wauters, Els, Boeckx, Bram, Aibar, Sara, Nittner, David, Burton, Oliver, Bassez, Ayse, Decaluwé, Herbert, Pircher, Andreas, Van den Eynde, Kathleen et al. · 2018 · Nature Medicine
36. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review
    Anna Chlingaryan, Salah Sukkarieh, Brett M. Whelan · 2018 · Computers and Electronics in Agriculture
37. Introduction to Machine Learning
    Fei Richard Yu, Ying He · 2019 · Springer briefs in electrical and computer engineering
38. An overview of anomaly detection techniques: Existing solutions and latest technological trends
    Animesh Patcha, Jung-Min Jerry Park · 2007 · Computer Networks
39. Reconciling modern machine-learning practice and the classical bias–variance trade-off
    Belkin, Mikhail, Hsu, Daniel, Ma, Siyuan, Mandal, Soumik · 2019 · Proceedings of the National Academy of Sciences
40. Decision trees: a recent overview
    Sotiris B. Kotsiantis · 2011 · Artificial Intelligence Review
41. CAESAR models for developmental toxicity
    Antonio Cassano, Alberto Manganaro, Todd M. Martin, Douglas Young, Nadège Piclin, Marco Pintore, Davide Bigoni, Emilio Benfenati · 2010 · Chemistry Central Journal
42. Recursive partitioning for heterogeneous causal effects
    Athey, Susan, Imbens, Guido · 2016 · Proceedings of the National Academy of Sciences
43. Global threat of arsenic in groundwater
    Joel E. Podgorski, Michael D. Vanden Berg · 2020 · Science
44. Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines
    Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., Chica-Rivas, M. · 2015 · Ore Geology Reviews
45. Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
    Johannes Stallkamp, Marc Schlipsing, Jan Salmen, Christian Igel · 2012 · Neural Networks
46. A comparison of random forest variable selection methods for classification prediction modeling
    Speiser, Jaime Lynn, Miller, Michael E., Tooze, Janet, Ip, Edward · 2019 · Expert Systems with Applications
47. Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis
    Jianguo Xia, David Scott Wishart · 2016 · Current Protocols in Bioinformatics
48. Learning Rich Features from RGB-D Images for Object Detection and Segmentation
    Gupta, Saurabh, Girshick, Ross, Arbeláez, Pablo, Malik, Jitendra · 2014 · Lecture Notes in Computer Science
49. Visual Tracking: An Experimental Survey
    Arnold W. M. Smeulders, Dung M. Chu, Rita Cucchiara, Simone Calderara, Afshin Dehghan, Mubarak A. Shah · 2014 · IEEE Transactions on Pattern Analysis and Machine Intelligence
50. Quantile Regression Forests
    Nicolai Meinshausen · 2006
51. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins
    Rouillard, Andrew D., Gundersen, Gregory W., Fernandez, Nicolas F., Wang, Zichen, Monteiro, Caroline D., McDermott, Michael G., Ma’ayan, Avi · 2016 · Database
52. Gut microbiome and serum metabolome alterations in obesity and after weight-loss intervention
    Liu, Ruixin, Hong, Jie, Xu, Xiaoqiang, Feng, Qiang, Zhang, Dongya, Gu, Yanyun, Shi, Juan, Zhao, Shaoqian, Liu, Wen, Wang, Xiaokai et al. · 2017 · Nature Medicine
53. Positive biodiversity-productivity relationship predominant in global forests
    Liang, Jingjing, Crowther, Thomas W., Picard, Nicolas, Wiser, Susan, Zhou, Mo, Alberti, Giorgio, Schulze, Ernst-Detlef, McGuire, A. David, Bozzato, Fabio, Pretzsch, Hans et al. · 2016 · Science
54. Antibiotics, birth mode, and diet shape microbiome maturation during early life
    Bokulich, Nicholas A., Chung, Jennifer, Battaglia, Thomas, Henderson, Nora, Jay, Melanie, Li, Huilin, D. Lieber, Arnon, Wu, Fen, Perez-Perez, Guillermo I., Chen, Yu et al. · 2016 · Science Translational Medicine
55. Evaluation of consensus methods in predictive species distribution modelling
    Mathieu Marmion, Miia Parviainen, Miska Luoto, Risto K. Heikkinen, Wilfried Thuiller · 2008 · Diversity and Distributions
56. Machine learning and data mining methods in diabetes research
    Kavakiotis, Ioannis, Tsave, Olga, Salifoglou, Athanasios, Maglaveras, Nicos, Vlahavas, Ioannis, Chouvarda, Ioanna · 2017 · Computational and Structural Biotechnology Journal
57. Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph
    Bagher Zadeh, AmirAli, Liang, Paul Pu, Poria, Soujanya, Cambria, Erik, Morency, Louis-Philippe · 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
58. A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects
    Ibomoiye Domor Mienye, Yanxia Sun · 2022 · IEEE Access
59. Cost-sensitive boosting for classification of imbalanced data
    Yanmin Sun, Mohamed S. Kamel, Andrew K. C. Wong, Yang Wang · 2007 · Pattern Recognition
60. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
    Rosenberg, Alexander B., Roco, Charles M., Muscat, Richard A., Kuchina, Anna, Sample, Paul, Yao, Zizhen, Graybuck, Lucas T., Peeler, David J., Mukherjee, Sumit, Chen, Wei et al. · 2018 · Science
61. The random forest algorithm for statistical learning
    Matthias Schonlau, Rosie Yuyan Zou · 2020 · The Stata Journal Promoting communications on statistics and Stata
62. Review of photovoltaic power forecasting
    J. Antonanzas, Natalia Osorio, Rodrigo A. Escobar, Ruben Urraca-Valle, Francisco Javier Martínez-de-Pisón, Fernando Antoñanzas-Torres · 2016 · Solar Energy
63. Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics
    Anindya Ghose, Panagiotis G. Ipeirotis · 2010 · IEEE Transactions on Knowledge and Data Engineering
64. A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
    Al-Garadi, Mohammed Ali, Mohamed, Amr, Al-Ali, Abdulla Khalid, Du, Xiaojiang, Ali, Ihsan, Guizani, Mohsen · 2020 · IEEE Communications Surveys & Tutorials
65. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications
    Jing Wei, Zhanqing Li, Alexei Lyapustin, Lin Sun, Yiran Peng, Wenhao Xue, Tianning Su, Maureen C. Cribb · 2020 · Remote Sensing of Environment
66. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
    Agrawal, Ankit, Choudhary, Alok · 2016 · APL Materials
67. Predictors of public climate change awareness and risk perception around the world
    Tien Ming Lee, Ezra M. Markowitz, Peter D. Howe, Chia‐Ying Ko, Anthony Leiserowitz · 2015 · Nature Climate Change
68. Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery
    Thanh Noi Phan, Martin W Kappas · 2017 · Sensors
69. Explaining nonlinear classification decisions with deep taylor decomposition
    Montavon, Grégoire, Lapuschkin, Sebastian, Binder, Alexander, Samek, Wojciech, Müller, Klaus-Robert · 2017 · Pattern Recognition
70. Leadership, social capital and incentives promote successful fisheries
    Nicolás L. Gutiérrez, Ray W. Hilborn, Omar Defeo · 2011 · Nature
71. American College of Rheumatology classification criteria for Sjögren's syndrome: A data‐driven, expert consensus approach in the Sjögren's International Collaborative Clinical Alliance Cohort
    Stephen C. Shiboski, Caroline H. Shiboski, Lindsey A. Criswell, Alan N. Baer, Stephen Challacombe, Hector Eduardo Lanfranchi, Morten Schiødt, Hisanori Umehara, Frederick B. Vivino, Yang Zhao et al. · 2012 · Arthritis Care & Research
72. Detecting gene–gene interactions that underlie human diseases
    Cordell, Heather J · 2009 · Nature Reviews Genetics
73. Can machine-learning improve cardiovascular risk prediction using routine clinical data?
    Weng, Stephen F., Reps, Jenna, Kai, Joe, Garibaldi, Jonathan M., Qureshi, Nadeem · 2017 · PLOS ONE
74. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots
    Samiran Banerjee, Florian Walder, Lucie Büchi, Marcel Meyer, Alain Valzano‐Held, Andreas Gattinger, Thomas Keller, Raphaël Charles, Marcel G. A. van der Heijden · 2019 · The ISME Journal
75. A large-scale hierarchical multi-view RGB-D object dataset
    Kevin Lai, Liefeng Bo, Xiaofeng Ren, Dieter Fox · 2011 · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings
76. Educational Data Mining and Learning Analytics
    Ryan S. Baker, Paul Salvador Inventado · 2014 · Springer eBooks
77. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst
    Jianguo Xia, David Scott Wishart · 2011 · Nature Protocols
78. Predictive Analytics in Information Systems Research1
    Shmueli, Galit, Koppius, Otto R. · 2011 · MIS Quarterly
79. Machine learning in manufacturing: Advantages, challenges, and applications
    Wuest, Thorsten, Weimer, Daniel, Irgens, Christopher, Thoben, Klaus-Dieter · 2016 · Production & Manufacturing Research
80. Predictive learning via rule ensembles
    Friedman, Jerome H., Popescu, Bogdan E. · 2008 · The Annals of Applied Statistics
81. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data
    Bjoern H. Menze, B. Michael Kelm, Ralf Masuch, Uwe Himmelreich, Peter Bachert, Wolfgang H. Petrich, Fred A. Hamprecht · 2009 · BMC Bioinformatics
82. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017
    Peng Gong, Han Liu, Meinan Zhang, Congcong Li, Jie Wang, Huabing Huang, Nicholas Clinton, Luyan Ji, Wenyu Li, Yuqi Bai et al. · 2019 · 中国科学通报：英文版
83. Salt-responsive gut commensal modulates TH17 axis and disease
    Wilck, Nicola, Matus, Mariana G., Kearney, Sean M., Olesen, Scott W., Forslund, Kristoffer, Bartolomaeus, Hendrik, Haase, Stefanie, Mähler, Anja, Balogh, András, Markó, Lajos et al. · 2017 · Nature
84. Untitled work
    2015 · neural information processing systems
85. Mapping forest canopy height globally with spaceborne lidar
    Marc Simard, Naiara Sardinha Pinto, Joshua B. Fisher, Alessandro Baccini · 2011 · Journal of Geophysical Research Atmospheres
86. BOOSTED TREES FOR ECOLOGICAL MODELING AND PREDICTION
    Glenn De’ath · 2007 · Ecology
87. Investigation of the random forest framework for classification of hyperspectral data
    J. Ham, Yangchi Chen, Melba M. Crawford, Joydeep Ghosh · 2005 · IEEE Transactions on Geoscience and Remote Sensing
88. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance
    Abdelaziz Merghadi, Ali P. Yunus, Jie Dou, Jim Whiteley, Binh Thai Pham, Dieu Tien Bui, Ram Avtar, Abderrahmane Boumezbeur · 2020 · Earth-Science Reviews
89. Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research
    Lessmann, Stefan, Baesens, Bart, Seow, Hsin-Vonn, Thomas, Lyn C. · 2015 · European Journal of Operational Research
90. Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture
    Bing Lu, Phuong D. Dao, Jiangui Liu, Yuhong He, Jiali Shang · 2020 · Remote Sensing
91. Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy
    Sharafaldin, Iman, Lashkari, Arash Habibi, Hakak, Saqib, Ghorbani, Ali A. · 2019 · 2019 International Carnahan Conference on Security Technology (ICCST)
92. Using data mining to model and interpret soil diffuse reflectance spectra
    Raphael A. Viscarra Rossel, Thorsten Behrens · 2010 · Geoderma
93. Machine learning for data-driven discovery in solid Earth geoscience
    Karianne J. Bergen, Paul Allan Johnson, Maarten V. de Hoop, Gregory C. Beroza · 2019 · Science
94. Persistent gut microbiota immaturity in malnourished Bangladeshi children
    Subramanian, Sathish, Huq, Sayeeda, Yatsunenko, Tanya, Haque, Rashidul, Mahfuz, Mustafa, Alam, Mohammed A., Benezra, Amber, DeStefano, Joseph, Meier, Martin F., Muegge, Brian D. et al. · 2014 · Nature
95. Text as Data
    M. Gentzkow, Bryan T. Kelly, Matt Taddy · 2017 · Journal of Economic Literature
96. Tumor-Associated Macrophages and Survival in Classic Hodgkin's Lymphoma
    Steidl, Christian, Lee, Tang, Shah, Sohrab P., Farinha, Pedro, Han, Guangming, Nayar, Tarun, Delaney, Allen, Jones, Steven J., Iqbal, Javeed, Weisenburger, Dennis D. et al. · 2010 · New England Journal of Medicine
97. Image Classification using Random Forests and Ferns
    Anna Bosch, Andrew Zisserman, Xavier Muñoz · 2007 · IEEE/CVF International Conference on Computer Vision (ICCV)
98. Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review
    Swapan Talukdar, Pankaj Singha, Susanta Mahato, Shahfahad, Swades Pal, Yuei‐An Liou, Atiqur Rahman · 2020 · Remote Sensing
99. Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings
    Stefan Lessmann, Bart Baesens, Christophe Mues, Swantje Pietsch · 2008 · IEEE Transactions on Software Engineering
100. Deep learning: new computational modelling techniques for genomics
     Gökçen Eraslan, Žiga Avsec, Julien Gagneur, Fabian Joachim Theis · 2019 · Nature Reviews Genetics