Bagging predictors

Cited by (1,119)

Items 701–800 of 1,119

1. A survey of data partitioning and sampling methods to support big data analysis
   Mohammad Sultan Mahmud, Joshua Zhexue Huang, Salman Salloum, Tamer Z. Emara, Kuanishbay Sadatdiynov · 2020 · Big Data Mining and Analytics
2. Prediction of surface chloride concentration of marine concrete using ensemble machine learning
   Rong Cai, Taihao Han, Wenyu Liao, Jie Huang, Dawang Li, Aditya Kumar, Hongyan Ma · 2020 · Cement and Concrete Research
3. On the overestimation of random forest’s out-of-bag error
   Silke Janitza, Roman Hornung · 2018 · PLoS ONE
4. Application of Random Forest Algorithm on Feature Subset Selection and Classification and Regression
   Jitendra Kumar Jaiswal, Rita Samikannu · 2017 · World Congress on Computing and Communication Technologies (WCCCT)
5. Digital mapping of soil carbon fractions with machine learning
   Hamza Keskin, Sabine Grunwald, Willie G. Harris · 2018 · Geoderma
6. A new machine learning model based on induction of rules for autism detection
   Fadi Thabtah, David Peebles · 2019 · Health Informatics Journal
7. Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data
   Arash Jahangiri, Hesham A. Rakha · 2015 · IEEE Transactions on Intelligent Transportation Systems
8. Does the Arterial Cannulation Site for Circulatory Arrest Influence Stroke Risk?
   Lars Georg Svensson, Eugene H. Blackstone, Jeevanantham Rajeswaran, Joseph F. Sabik, Bruce Whitney Lytle, Gonzalo V. Gonzalez‐Stawinski, Poseidon S. Varvitsiotis, Michael K. Banbury, Patrick M. McCarthy, Gösta Pettersson et al. · 2004 · The Annals of Thoracic Surgery
9. Generalized Shrinkage Methods for Forecasting Using Many Predictors
   James H. Stock, Mark W. Watson · 2012 · Journal of Business and Economic Statistics
10. Automatic analysis of diabetic peripheral neuropathy using multi-scale quantitative morphology of nerve fibres in corneal confocal microscopy imaging
    Mohammad A. Dabbah, James Graham, Ioannis Nikolaos Petropoulos, Mitra Tavakoli, Rayaz Ahmed Malik · 2011 · Medical Image Analysis
11. Advanced Statistical Matrices for Texture Characterization: Application to Cell Classification
    Guillaume Thibault, Jesús Angulo, Fernand Meyer · 2013 · IEEE Transactions on Biomedical Engineering
12. A constructive algorithm for training cooperative neural network ensembles
    Md. Monirul Islam, Xin Yao, Kazuyuki Murase · 2003 · IEEE Transactions on Neural Networks
13. Learning to Extract Keyphrases from Text
    Turney, Peter · 1999 · arXiv (Cornell University)
14. Real-time sleep apnea detection by classifier combination
    Xie, B., Hlaing Minn · 2012 · IEEE Transactions on Information Technology in Biomedicine
15. Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines
    Fabian Löw, Ulrich Michel, Stefan W. Dech, Christopher Conrad · 2013 · ISPRS Journal of Photogrammetry and Remote Sensing
16. A comparison of models for predicting early hospital readmissions
    Joseph Futoma, Jonathan D. Morris, Joseph E. Lucas · 2015 · Journal of Biomedical Informatics
17. Analogue Methods in Palaeoecology: Using theanaloguePackage
    Gavin L. Simpson · 2007 · Journal of Statistical Software
18. Mapping forest change using stacked generalization: An ensemble approach
    Sean P. Healey, Warren B. Cohen, Zhiqiang Yang, C. Kenneth Brewer, Evan B. Brooks, Noel Gorelick, Alexander J. Hernández, Chengquan Huang, M. Joseph Hughes, Robert E. Kennedy et al. · 2017 · Remote Sensing of Environment
19. Interpretable Ensemble-Machine-Learning models for predicting creep behavior of concrete
    Minfei Liang, Ze Chang, Zhi Wan, Yidong Gan, Erik Schlangen, Branko Šavija · 2021 · Cement and Concrete Composites
20. A Winnow-Based Approach to Context-Sensitive Spelling Correction
    Andrew R. Golding, Dan Roth · 1999 · Machine Learning
21. On the nature of cavities on protein surfaces: Application to the identification of drug‐binding sites
    Murad Nayal, Barry H. Honig · 2006 · Proteins Structure Function and Bioinformatics
22. Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey
    Moziihrii Ado, Khwairakpam Amitab, Arnab Kumar Maji, Elżbieta Jasińska, Radomír Goňo, Zbigniew M. Leonowicz, Michał Jasiński · 2022 · Remote Sensing
23. Creating diversity in ensembles using artificial data
    Prem Melville, Raymond J. Mooney · 2004 · Information Fusion
24. Spatial downscaling of precipitation using adaptable random forests
    Xiaogang He, Nathaniel W. Chaney, Marc Schleiss, Justin Sheffield · 2016 · Water Resources Research
25. Propensity-Matched Comparisons of Clinical Outcomes After Transapical or Transfemoral Transcatheter Aortic Valve Replacement: A Placement of Aortic Transcatheter Valves (PARTNER)-I Trial Substudy
    Blackstone, Eugene H., Suri, Rakesh M., Rajeswaran, Jeevanantham, Babaliaros, Vasilis, Douglas, Pamela S., Fearon, William F., Miller, D. Craig, Hahn, Rebecca T., Kapadia, Samir, Kirtane, Ajay J. et al. · 2015 · Circulation
26. Ensembles of Learning Machines
    Giorgio Valentini, Francesco Masulli · 2002 · Lecture notes in computer science
27. New Applications of Ensembles of Classifiers
    R. Barandela, Rosa María Valdovinos Rosas, J. Salvador Snchez · 2003 · Pattern Analysis and Applications
28. Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods
    Dieu Tien Bui, Paraskevas Tsangaratos, Phuong Thao Thi Ngo, Tien Dat Pham, Binh Thai Pham · 2019 · The Science of The Total Environment
29. Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective
    Chollette C. Olisah, Lyndon Neal Smith, Melvyn Lionel Smith · 2022 · Computer Methods and Programs in Biomedicine
30. Leaf disease detection using machine learning and deep learning: Review and challenges
    Chittabarni Sarkar, Deepak Gupta, Umesh Chandra Gupta, Barenya Bikash Hazarika · 2023 · Applied Soft Computing
31. An ensemble scheme based on language function analysis and feature engineering for text genre classification
    Aytuğ Onan · 2016 · Journal of Information Science
32. A data-driven predictive model of city-scale energy use in buildings
    Kontokosta, Constantine E., Tull, Christopher · 2017 · Applied Energy
33. Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study
    Fan, Yong, Resnick, Susan M., Wu, Xiaoying, Davatzikos, Christos · 2008 · NeuroImage
34. An Adaptive Version of the Boost by Majority Algorithm
    Yoav Freund · 2001 · Machine Learning
35. Wavelet neural networks: A practical guide
    Antonis Alexandridis, Achilleas D. Zapranis · 2013 · Neural Networks
36. TPZ: photometric redshift PDFs and ancillary information by using prediction trees and random forests
    M. Carrasco Kind, Robert J. Brunner · 2013 · Monthly Notices of the Royal Astronomical Society
37. Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage
    Sarker, Iqbal H., Kayes, A. S. M., Watters, Paul · 2019 · Journal of Big Data
38. Forecasting and operational research: a review
    Fildes, R, Nikolopoulos, K, Crone, S F, Syntetos, A A · 2008 · Journal of the Operational Research Society
39. Comparing the determinants of cropland abandonment in Albania and Romania using boosted regression trees
    Daniel Müller, Pedro J. Leitão, Thomas O. Sikor · 2013 · Agricultural Systems
40. On the relationship between feature selection and classification accuracy
    Andreas Janecek, Wilfried N. Gansterer, Michael Demel, Gerhard Franz Ecker · 2008 · Phaidra (Universität Wien)
41. Functional Trees
    João Manuel Portela da Gama · 2004 · Machine Learning
42. Forecasting tourism demand with multisource big data
    Hengyun Li, Mingming Hu, Gang Li · 2020 · Annals of Tourism Research
43. Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data
    Kardani, Navid, Zhou, Annan, Nazem, Majidreza, Shen, Shui-Long · 2021 · Journal of Rock Mechanics and Geotechnical Engineering
44. Tree ensembles for predicting structured outputs
    Dragi Kocev, Celine Vens, Jan Struyf, Sašo Džeroski · 2012 · Pattern Recognition
45. General conditions for predictivity in learning theory
    Tomaso Poggio, Ryan M. Rifkin, Sayan Mukherjee, Partha Niyogi · 2004 · Nature Cell Biology
46. Data-derived soft-sensors for biological wastewater treatment plants: An overview
    Henri Haimi, Michela Mulas, Francesco Corona, Riku Vahala · 2013 · Environmental Modelling & Software
47. A Simple and Effective Model-Based Variable Importance Measure
    Brandon Greenwell, Bradley C. Boehmke, Andrew McCarthy · 2018 · arXiv (Cornell University)
48. Prediction of cement-based mortars compressive strength using machine learning techniques
    Panagiotis G. Asteris, Mohammadreza Koopialipoor, Danial Jahed Armaghani, Evgenios A. Kotsonis, Paulo Barbosa Lourenço · 2021 · Neural Computing and Applications
49. Late risk of outcomes for adults with repaired tetralogy of Fallot from an inception cohort spanning four decades.
    Hickey, Edward J., Veldtman, Gruschen, Bradley, Timothy J., Gengsakul, Aungkana, Manlhiot, Cedric, Williams, William G., Webb, Gary D., McCrindle, Brian W. · 2009 · European Journal of Cardio-Thoracic Surgery
50. Random Forests for Genetic Association Studies
    Benjamin Alan Goldstein, Eric C. Polley, Farren B. S. Briggs · 2011 · Statistical Applications in Genetics and Molecular Biology
51. Machine Learning–Based Failure Mode Recognition of Circular Reinforced Concrete Bridge Columns: Comparative Study
    Sujith Mangalathu, Jong‐Su Jeon · 2019 · Journal of Structural Engineering
52. iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization
    Zhen Chen, Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yongzi Chen, Tatsuya Akutsu, Roger J. Daly, Geoffrey I. Webb, Quanzhi Zhao et al. · 2021 · Nucleic Acids Research
53. Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks
    Emmert-Streib, Frank, Dehmer, Matthias, Haibe-Kains, Benjamin · 2014 · Frontiers in Cell and Developmental Biology
54. Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR
    Nicholas James Tustison, K. L. Shrinidhi, Max Wintermark, Christopher R. Durst, Benjamin M. Kandel, James C. Gee, Murray Grossman, Brian Avants · 2014 · Neuroinformatics
55. Valve repair versus valve replacement for degenerative mitral valve disease
    Alan Marc Gillinov, Eugene H. Blackstone, Edward R. Nowicki, Worawong Slisatkorn, Ghannam A. Al‐Dossari, Douglas R. Johnston, Kristopher M. George, Penny L. Houghtaling, Brian P. Griffin, Joseph F. Sabik et al. · 2008 · Journal of Thoracic and Cardiovascular Surgery
56. A CLUE for CLUster Ensembles
    Kurt Hornik · 2005 · Journal of Statistical Software
57. Interpretation of nonlinear relationships between process variables by use of random forests
    Lidia Auret, Chris Aldrich · 2012 · Minerals Engineering
58. A machine learning framework for customer purchase prediction in the non-contractual setting
    Andrés Martínez, Claudia Schmuck, Sergiy Pereverzyev, Clemens Pirker, Markus Haltmeier · 2018 · European Journal of Operational Research
59. DeepCropMapping: A multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping
    Jinfan Xu, Yue Zhu, Renhai Zhong, Zhixian Lin, Jialu Xu, Hao Jiang, Jingfeng Huang, Haifeng Li, Tao Lin · 2020 · Remote Sensing of Environment
60. Email Spam Filtering: A Systematic Review
    Gordon V. Cormack · 2008 · Foundations and Trends® in Information Retrieval
61. Randomizing Outputs to Increase Prediction Accuracy
    Leo Breiman · 2000 · Machine Learning
62. Recent Advances of Large-Scale Linear Classification
    Guo-Xun Yuan, Chia-Hua Ho, Chih‐Jen Lin · 2012 · Proceedings of the IEEE
63. Cancer classification using gene expression data
    Ying Lu, Jiawei Han · 2003 · Information Systems
64. Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination
    Eugene Tuv, Alexander Borisov, George C. Runger, Kari Torkkola · 2009
65. A comparison among interpretative proposals for Random Forests
    Massimo Aria, Corrado Cuccurullo, Agostino Gnasso · 2021 · Machine Learning with Applications
66. A systematic literature review for network intrusion detection system (IDS)
    Abdulganiyu, Oluwadamilare Harazeem, Ait Tchakoucht, Taha, Saheed, Yakub Kayode · 2023 · International Journal of Information Security
67. Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm
    Emmanuel John M. Carranza, Alice G. Laborte · 2014 · Ore Geology Reviews
68. Generating Accurate and Diverse Members of a Neural-Network Ensemble
    David W. Opitz, JUDE W. SHAVLIK · 1995
69. Multivariate random forests
    Mark Robert Segal, Yuanyuan Xiao · 2011 · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
70. Machine learning-driven credit risk: a systemic review
    Si Shi, Rita T. Tse, Wuman Luo, Stefano d’Addona, Giovanni Pau · 2022 · Neural Computing and Applications
71. EMOTION IN SPEECH: RECOGNITION AND APPLICATION TO CALL CENTERS
    Valery A. Petrushin · 1999
72. Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning
    Shaokang Hou, Yaoru Liu, Qiang Yang · 2021 · Journal of Rock Mechanics and Geotechnical Engineering
73. Computer-aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregating
    Ahnaf Rashik Hassan, Mohammed Imamul Hassan Bhuiyan · 2015 · Biomedical Signal Processing and Control
74. Ensemble with neural networks for bankruptcy prediction
    Myoung-Jong Kim, Dae-Ki Kang · 2009 · Expert Systems with Applications
75. Aggregate features and ADABOOST for music classification
    James Bergstra, Norman Casagrande, Dumitru Erhan, Douglas Eck, Balázs Kégl · 2006 · Machine Learning
76. Consistency of random survival forests
    Hemant Ishwaran, Udaya B. Kogalur · 2010 · Statistics & Probability Letters
77. Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods
    Gretchen G. Moisen, Elizabeth A. Freeman, Jock A. Blackard, Tracey S. Frescino, Niklaus E. Zimmermann, Thomas C. Edwards · 2006 · Ecological Modelling
78. Crowdsourcing reproducible seizure forecasting in human and canine epilepsy
    Benjamin H. Brinkmann, Joost Wagenaar, Drew Abbot, Phillip Adkins, Simone C. Bosshard, Min Chen, Quang Minh Tieng, Jialune He, Francisco Javier Muñoz–Almaraz, Paloma Botella-Rocamora et al. · 2016 · Brain
79. Distributed Learning in Wireless Sensor Networks
    Joel B. Predd, Sanjeev R. Kulkarni, H. Vincent Poor · 2007 · Wiley eBooks
80. Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction
    Amirhosein Mosavi, Farzaneh Sajedi Hosseini, Bahram Choubin, Massoud Goodarzi, Adrienn A. Dineva, Elham Rafiei Sardooi · 2020 · Water Resources Management
81. Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
    Venkat Pavan Nemani, Luca Biggio, Xun Huan, Zhen Yu Hu, Olga Fink, Anh Tran, Yan Wang, Xiaoge Zhang, Chao Kun Hu · 2023 · Mechanical Systems and Signal Processing
82. Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis
    Chong, Yolanda T., Koh, Judice L.Y., Friesen, Helena, Kaluarachchi Duffy, Supipi, Cox, Michael J., Moses, Alan, Moffat, Jason, Boone, Charles, Andrews, Brenda J. · 2015 · Cell
83. Semi-supervised self-training for decision tree classifiers
    Jafar Tanha, Maarten van Someren, Hamideh Afsarmanesh · 2015 · International Journal of Machine Learning and Cybernetics
84. Estimating Global Cropland Extent with Multi-year MODIS Data
    Kyle W. Pittman, Matthew C. Hansen, Inbal Becker‐Reshef, Peter V. Potapov, Christopher O. Justice · 2010 · Remote Sensing
85. Exploratory Under-Sampling for Class-Imbalance Learning
    Xuying Liu, Jianxin Wu, Zhi-hua Zhou · 2006 · Proceedings
86. Challenges in discriminating profanity from hate speech
    Shervin Malmasi, Marcos Zampieri · 2017 · Journal of Experimental & Theoretical Artificial Intelligence
87. A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles
    Luc Le Mero, Dewei Yi, Mehrdad Dianati, Alexandros Mouzakitis · 2022 · IEEE Transactions on Intelligent Transportation Systems
88. Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology
    Eric Warren Fox, Ryan A. Hill, Scott G. Leibowitz, Anthony R. Olsen, Darren J. Thornbrugh, Marc H. Weber · 2017 · Environmental Monitoring and Assessment
89. A review of Earth Artificial Intelligence
    Sun, Ziheng, Sandoval, Laura, Crystal-Ornelas, Robert, Mousavi, S. Mostafa, Wang, Jinbo, Lin, Cindy, Cristea, Nicoleta, Tong, Daniel, Carande, Wendy Hawley, Ma, Xiaogang et al. · 2022 · Computers & Geosciences
90. Machine Learning Techniques and Chi-Square Feature Selection for Cancer Classification Using SAGE Gene Expression Profiles
    Xin Jin, Anbang Xu, Rongfang Bie, Ping Guo · 2006 · Lecture notes in computer science
91. PLANET
    Biswanath Panda, Joshua S. Herbach, Sugato Basu, Roberto J. Bayardo · 2009 · Proceedings of the VLDB Endowment
92. Enriching speech recognition with automatic detection of sentence boundaries and disfluencies
    Yang Liu, Elizabeth E. Shriberg, Andreas Stolcke, Dustin Hillard, Mari Ostendorf E, Mary P. Harper · 2006 · IEEE Transactions on Audio Speech and Language Processing
93. Deep reinforcement learning in computer vision: a comprehensive survey
    Ngan Le, Vidhiwar Singh Rathour, Kashu Yamazaki, Khoa Luu, Marios Savvides · 2021 · Artificial Intelligence Review
94. Mining opinions from instructor evaluation reviews: a deep learning approach
    Onan, Aytuğ · 2020 · Computer Applications in Engineering Education
95. Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival
    Hemant Ishwaran, Min Lü · 2018 · Statistics in Medicine
96. Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement
    Ernest Kwame Ampomah, Zhiguang Qin, Gabriel Nyame · 2020 · Information
97. On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data
    Schwarz, Daniel F., König, Inke R., Ziegler, Andreas · 2010 · Bioinformatics
98. Bootstraps for Time Series
    Peter Bühlmann · 2002 · Statistical Science
99. Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set
    Iurii Sushko, Sergii Novotarskyi, Robert Körner, Anil Kumar Pandey, Artem R Cherkasov, Jiazhong Li, Paola Gramatica, Katja Hansen, Timon Sebastian Schroeter, Klaus‐Robert Müller et al. · 2010 · Journal of Chemical Information and Modeling
100. Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning
     Lukas Knoll, Lutz Breuer, Martin Bach · 2019 · The Science of The Total Environment