Bagging predictors

Cited by (1,119)

Items 501–600 of 1,119

1. Resampling-Based Ensemble Methods for Online Class Imbalance Learning
   Shuo Wang, Leandro Lei Minku, Xin Yao · 2014 · IEEE Transactions on Knowledge and Data Engineering
2. Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques
   Jui‐Sheng Chou, Chien‐Kuo Chiu, Mahmoud E. Farfoura, Ismail Adel AL-Taharwa · 2010 · Journal of Computing in Civil Engineering
3. Ecology of butterflies in Europe
   2010 · Choice Reviews Online
4. Data Mining With R: Learning By Case Studies
   Luı́s Torgo · 2010
5. Ensemble deep learning for regression and time series forecasting
   Xueheng Qiu, Le Zhang, Ye Ren, Ponnuthurai Nagaratnam Suganthan, Gehan A. J. Amaratunga · 2014
6. Building a dynamic reputation system for DNS
   Manos Antonakakis, Roberto Perdisci, David Dagon, Wenke Lee, Nick Feamster · 2010
7. Selection-channel-aware rich model for Steganalysis of digital images
   Tomáš Denemark, Vahid Sedighi, Vojtěch Holub, Rémi Cogranne, Jessica Fridrich · 2014 · IEEE International Workshop on Information Forensics and Security
8. Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra
   Gustavo M. Vasques, Sabine Grunwald, J.O. Sickman · 2008 · Geoderma
9. Machine Learning and Deep Learning in Energy Systems: A Review
   Mohammad Mahdi Forootan, Iman Larki, Rahim Zahedi, Abolfazl Ahmadi · 2022 · Sustainability
10. 3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture
    Dan T. D. Nguyen, Xun Jia, David Sher, Mu-Han Lin, Zohaib Iqbal, Hui Liu, Steve B Jiang · 2019 · Physics in Medicine and Biology
11. Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
    Mansour Ahmadi, Dmitry Ulyanov, Stanislav Semenov, Mikhail Trofimov, Giorgio Giacinto · 2016 · ACM Conference on Data and Application Security and Privacy (CODASPY)
12. Computational design optimization of concrete mixtures: A review
    M.A. DeRousseau, Joseph R. Kasprzyk, Wil V. Srubar · 2018 · Cement and Concrete Research
13. Machine Learning for Android Malware Detection Using Permission and API Calls
    Naser Peiravian, Xingquan Zhu A · 2013 · IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
14. The relative performance of ensemble methods with deep convolutional neural networks for image classification
    Cheng Ju, Aurélien Bibaut, Mark J. van der Laan · 2018 · Journal of Applied Statistics
15. Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life
    Chao Kun Hu, Byeng Dong Youn, Pingfeng Wang, Joung Taek Yoon · 2012 · Reliability Engineering & System Safety
16. A deep learning ensemble approach for crude oil price forecasting
    Yang Zhao, Jianping Li, Lean Yu · 2017 · Energy Economics
17. Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates
    Jie Sun, Jie Lang, Hamido Fujita, Hui Li · 2017 · Information Sciences
18. Sentiment classification: The contribution of ensemble learning
    Gang Wang, Jianshan Sun, Jian Ma, Kaiquan S. J. Xu, Jibao Gu · 2013 · Decision Support Systems
19. Distributed learning in wireless sensor networks
    Joel B. Predd, Sanjeev R. Kulkarni, H. Vincent Poor · 2006 · IEEE Signal Processing Magazine
20. Improving Melanoma Classification by Integrating Genetic and Morphologic Features
    Amaya Virós, Jane Fridlyand, Jürgen M. Bauer, Konstantin Lasithiotakis, Claus Garbe, Daniel Pinkel, Boris C. Bastian · 2008 · PLoS Medicine
21. Mining corporate annual reports for intelligent detection of financial statement fraud – A comparative study of machine learning methods
    Petr Hájek, Roberto Henriques · 2017 · Knowledge-Based Systems
22. Evapotranspiration evaluation models based on machine learning algorithms—A comparative study
    Francesco Granata · 2019 · Agricultural Water Management
23. Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation
    Julian Zilly, Joachim M. Buhmann, Dwarikanath Mahapatra · 2016 · Computerized Medical Imaging and Graphics
24. Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery
    Te Han, Dongxiang Jiang, Qi Zhao, Lei Wang, Kai Yin · 2017 · Transactions of the Institute of Measurement and Control
25. Fault Diagnosis of a Rolling Bearing Using Wavelet Packet Denoising and Random Forests
    Ziwei Wang, Qinghua Zhang, Jianbin Xiong, Ming Xiao, Guoxi Sun, He Jun · 2017 · IEEE Sensors Journal
26. KBA: kernel boundary alignment considering imbalanced data distribution
    Wu, G., Chang, E.Y. · 2005 · IEEE Transactions on Knowledge and Data Engineering
27. Bagging, Boosting and Ensemble Methods
    Peter Bühlmann · 2011 · Springer eBooks
28. Autonomously semantifying wikipedia
    Fei Qing Wu, Daniel S. Weld · 2007 · ACM International Conference on Information and Knowledge Management (CIKM)
29. Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
    Ming Li, Zhi‐Hua Zhou · 2007 · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans
30. BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning
    Yeming Wen, Dustin Tran, Jimmy Lei Ba · 2020 · arXiv (Cornell University)
31. Application of machine learning to construction injury prediction
    Antoine J.‐P. Tixier, Matthew Ryan Hallowell, Balaji Rajagopalan, Dean Bowman · 2016 · Automation in Construction
32. Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife
    Stefan Wager, Trevor J. Hastie, Efron, Bradley · 2013 · PubMed
33. Data Fusion and Remote Sensing: An ever-growing relationship
    Michael Schmitt, Xiao Xiang Zhu · 2016 · IEEE Geoscience and Remote Sensing Magazine
34. Series in Machine Perception and Artificial Intelligence
    2012 · Series in machine perception and artificial intelligence
35. Variable Selection with Error Control: Another Look at Stability Selection
    Rajen D. Shah, Richard J. Samworth · 2012 · Journal of the Royal Statistical Society Series B (Statistical Methodology)
36. Dynamically Weighted Balanced Loss: Class Imbalanced Learning and Confidence Calibration of Deep Neural Networks
    K. Ruwani M. Fernando, Chris P. Tsokos · 2021 · IEEE Transactions on Neural Networks and Learning Systems
37. COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements
    Yong Fan A, Dinggang G. Shen, Ruben C. Gur, Raquel E. Gur, Christos A. Davatzikos · 2007 · IEEE Transactions on Medical Imaging
38. Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation
    Torgyn Shaikhina, Dave Lowe, Sunil Daga, David Briggs, Robert M. Higgins, Natasha A. Khovanova · 2017 · Biomedical Signal Processing and Control
39. Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods
    Binh Thai Pham, Dieu Tien Bui, Hamid Reza Pourghasemi, Indra Prakash, M. B. Dholakia · 2015 · Theoretical and Applied Climatology
40. A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification
    Aytuğ Onan, Serdar Korukoğlu, Hasan Bulut · 2016 · Expert Systems with Applications
41. Artificial intelligence, cyber-threats and industry 4.0: challenges and opportunities
    Bécue, Adrien, Praça, Isabel, Gama, João · 2021 · Artificial Intelligence Review
42. An LSTM-based aggregated model for air pollution forecasting
    Yue‐Shan Chang, Hsin-Ta Chiao, Satheesh Abimannan, Yo‐Ping Huang, Yi-Ting Tsai, Kuan-Ming Lin · 2020 · Atmospheric Pollution Research
43. Variable Importance Plots—An Introduction to the vip Package
    M. Greenwell Brandon, C. Boehmke Bradley · 2020 · The R Journal
44. Using meteorological normalisation to detect interventions in air quality time series
    Stuart K. Grange, David C. Carslaw · 2018 · The Science of The Total Environment
45. Ensemble deep learning in bioinformatics
    Yue Cao, Thomas A. Geddes, Jean Yee Hwa Yang, Pengyi Yang · 2020 · Nature Machine Intelligence
46. Metalearning: A survey of trends and technologies
    Lemke, Christiane, Budka, Marcin, Gabrys, Bogdan · 2015 · Artificial Intelligence Review
47. Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area
    Meriame Mohajane, Romulus Dumitru Costache, Firoozeh Karimi, Quoc Bao Pham, Ali Essahlaoui, Hoang Nguyen, Giovanni Laneve, Fatiha Oudija · 2021 · Ecological Indicators
48. Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models
    Ghotra, Baljinder, McIntosh, Shane, Hassan, Ahmed E. · 2015 · 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering
49. REGRESSION TREES WITH UNBIASED VARIABLE SELECTION AND INTERACTION DETECTION
    Wei‐Yin Loh · 2002
50. Heterogeneous Face Recognition Using Kernel Prototype Similarities
    Brendan F. Klare, Anil K. Jain · 2012 · IEEE Transactions on Pattern Analysis and Machine Intelligence
51. The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling
    Yile Ao, Hongqi Li, Liping Zhu, Sikandar Ali, Zhongguo Yang · 2018 · Journal of Petroleum Science and Engineering
52. Research Review for Broad Learning System: Algorithms, Theory, and Applications
    Xinrong Gong, Tong Zhang, C. L. Philip Chen, Zhulin Liu · 2021 · IEEE Transactions on Cybernetics
53. Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Under Varying Imbalance Levels
    Mehdi Imani, Ali Beikmohammadi, Hamid Reza Arabnia · 2025 · Technologies
54. Lung cancer cell identification based on artificial neural network ensembles
    Zhi‐Hua Zhou, Yuan Ying Jiang, Yu-Bin Yang, Shifu Chen · 2002 · Artificial Intelligence in Medicine
55. Review on remote sensing methods for landslide detection using machine and deep learning
    Amrita Mohan, Amit Kumar Singh, Basant Kumar, R. S. Dwivedi · 2020 · Transactions on Emerging Telecommunications Technologies
56. Estimation and Accuracy After Model Selection
    Bradley Efron · 2013 · Journal of the American Statistical Association
57. Recognizing Depression from Twitter Activity
    Sho Tsugawa, Yusuke Kikuchi, Fumio Kishino, Kosuke Nakajima, Yuichi Itoh, Hiroyuki Ohsaki · 2015 · CHI Conference on Human Factors in Computing Systems
58. Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
    Urban Fasel, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton · 2022 · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
59. Active learning for logistic regression: an evaluation
    Andrew Ian Schein, Lyle Ungar · 2007 · Machine Learning
60. A bagging SVM to learn from positive and unlabeled examples
    Fantine Mordelet, Jean‐Philippe Vert · 2013 · Pattern Recognition Letters
61. The Genetic Architecture Of Maize Height
    Jason A. Peiffer, M. Cinta Romay, Michael A. Gore, Sherry A. Flint-Garcia, Zhiwu Zhang, Mark J. Millard, Candice A. C. Gardner, Michael D. McMullen, James Brendan Holland, Peter J. Bradbury et al. · 2014 · Genetics
62. Multi-criteria characterization of recent digital soil mapping and modeling approaches
    Sabine Grunwald · 2009 · Geoderma
63. Blockchain-Enabled Federated Learning Data Protection Aggregation Scheme With Differential Privacy and Homomorphic Encryption in IIoT
    Bin Jia, Xiaosong Zhang, Jiewen Liu, Yang Zhang, Ke Huang, Yongquan Liang · 2021 · IEEE Transactions on Industrial Informatics
64. Challenges in real-life emotion annotation and machine learning based detection
    Laurence Y. Devillers, Laurence Vidrascu, Lori F Lamel · 2005 · Neural Networks
65. Machine Learning-Based Fault Diagnosis for Single- and Multi-Faults in Induction Motors Using Measured Stator Currents and Vibration Signals
    Mohammad Zawad Ali, Md Nasmus Sakib Khan Shabbir, Xiaodong Liang, Yu Zhang, Ting Hu · 2019 · IEEE Transactions on Industry Applications
66. Where to find my next passenger
    Jing Yuan, Yu Zheng, Liuhang Zhang, Xing Xie, Guangzhong Sun · 2011 · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)
67. Machine Learning for Information Extraction in Informal Domains
    Dayne Freitag · 2000 · Machine Learning
68. Evaluation of machine learning methods for formation lithology identification: A comparison of tuning processes and model performances
    Yunxin Xie, Chenyang Zhu, Wen Xin Zhou, Zhongdong Li, Xuan Liu, Mei Tu · 2017 · Journal of Petroleum Science and Engineering
69. Propensity Scores
    Jason K. Luellen, William R. Shadish, Margaret H. Clark · 2005 · Evaluation Review
70. SVM and SVM Ensembles in Breast Cancer Prediction
    Min‐Wei Huang, Chih-Wen Chen, Wei‐Chao Lin, Shih‐Wen Ke, Chih‐Fong Tsai · 2017 · PLoS ONE
71. Semi-automated screening of biomedical citations for systematic reviews
    Wallace, Byron C, Trikalinos, Thomas A, Lau, Joseph, Brodley, Carla, Schmid, Christopher H · 2010 · BMC Bioinformatics
72. Optimal prediction pools
    John F Geweke, Gianni Amisano · 2011 · Journal of Econometrics
73. Outlier detection by active learning
    Naoki Abe, Bianca Zadrozny, John C. Langford · 2006 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
74. The structural and functional signatures of proteins that undergo multiple events of post‐translational modification
    Pejaver, Vikas, Hsu, Wei‐Lun, Xin, Fuxiao, Dunker, A. Keith, Uversky, Vladimir N., Radivojac, Predrag · 2014 · Protein Science
75. Adaptive Signature Design: An Adaptive Clinical Trial Design for Generating and Prospectively Testing A Gene Expression Signature for Sensitive Patients
    Boris Freidlin, Richard M Simon · 2005 · Clinical Cancer Research
76. The combining classifier: to train or not to train?
    Robert P. W. Duin · 2003
77. Protein flexibility and intrinsic disorder
    Predrag Radivojac A, Zoran Obradovic A, David Keith Smith, Guang Zhu, Slobodan Vučetić, Celeste J. Brown, J. David Lawson, A. Keith Dunker · 2003 · Protein Science
78. Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation
    Christoph Bergmeir, Rob J. Hyndman, José M. Benítez · 2016 · International Journal of Forecasting
79. Adaptive boosting for SAR automatic target recognition
    Yijun Sun, Zhipeng Liu, Siniša Todorović, Jian Li · 2007 · IEEE Transactions on Aerospace and Electronic Systems
80. Steel defect classification with Max-Pooling Convolutional Neural Networks
    Masci, Jonathan, Meier, Ueli, Ciresan, Dan, Schmidhuber, Jurgen, Fricout, Gabriel · 2012 · The 2012 International Joint Conference on Neural Networks (IJCNN)
81. Temporal Onset, Risk Factors, and Outcomes Associated With Stroke After Coronary Artery Bypass Grafting
    Khaldoun G. Tarakji · 2011 · JAMA
82. Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries Using Sparse Linear Regression
    Charles K. Fisher, Pankaj Mehta · 2014 · PLoS ONE
83. Classification with class imbalance problem: A review
    Aida Ali, Siti Mariyam Shamsuddin, Anca Ralescu · 2015
84. Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials
    Sun, Wenbo, Zheng, Yujie, Yang, Ke, Zhang, Qi, Shah, Akeel A., Wu, Zhou, Sun, Yuyang, Feng, Liang, Chen, Dongyang, Xiao, Zeyun et al. · 2019 · Science Advances
85. Dynamic selection of classifiers—A comprehensive review
    Alceu S. Britto, Robert Sabourin, Luiz S. Oliveira · 2014 · Pattern Recognition
86. Gradient modeling of conifer species using random forests
    Jeffrey S. Evans, Samuel A. Cushman · 2009 · Landscape Ecology
87. Review of Classification Methods on Unbalanced Data Sets
    Le Wang, Meng Han, Xiaojuan Li, Ni Zhang, Haodong Cheng · 2021 · IEEE Access
88. Transcatheter (TAVR) versus surgical (AVR) aortic valve replacement: Occurrence, hazard, risk factors, and consequences of neurologic events in the PARTNER trial
    David Craig Miller, Eugene H. Blackstone, Michael J. Mack, Lars Georg Svensson, Susheel Kodali, Samir Ramesh Kapadia, Jeevanantham Rajeswaran, William N. Anderson, Jeffrey W. Moses, Emin Murat Tuzcu et al. · 2012 · Journal of Thoracic and Cardiovascular Surgery
89. Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning
    Jonathan Schmidt, Jingming Shi, Pedro Borlido, Liming Chen, Silvana Botti, Miguel A. L. Marques · 2017 · Chemistry of Materials
90. Application of eXtreme gradient boosting trees in the construction of credit risk assessment models for financial institutions
    Yung-Chia Chang, Kuei‐Hu Chang, Guan-Jhih Wu · 2018 · Applied Soft Computing
91. Predicting property prices with machine learning algorithms
    Winky K.O. Ho, Bo Tang, Siu Wai Wong · 2020 · Journal of Property Research
92. The dropout learning algorithm
    Baldi, Pierre, Sadowski, Peter · 2014 · Artificial Intelligence
93. Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds
    Julie Shortridge, Seth David Guikema, Benjamin F. Zaitchik · 2016 · Hydrology and earth system sciences
94. Finding advertising keywords on web pages
    Wen-tau Yih, Joshua Goodman, Vitor R. Carvalho · 2006 · International World Wide Web Conference (WWW)
95. Early Detection of Alzheimer’s Disease Using Magnetic Resonance Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning
    Dan Pan, An Zeng, Longfei Jia, Yin Huang, Tory O. Frizzell, Xiaowei Song · 2020 · Frontiers in Neuroscience
96. Neural network ensemble strategies for financial decision applications
    David West, Scott A. Dellana, Jingxia Qian · 2004 · Computers & Operations Research
97. Under-sampling class imbalanced datasets by combining clustering analysis and instance selection
    Chih‐Fong Tsai, Wei‐Chao Lin, Ya‐Han Hu, Guan-Ting Yao · 2018 · Information Sciences
98. Exposing the Limitations of Molecular Machine Learning with Activity Cliffs
    van Tilborg, Derek, Alenicheva, Alisa, Grisoni, Francesca · 2022 · Journal of Chemical Information and Modeling
99. Ensemble of online sequential extreme learning machine
    Yuan Lan, Yeng Chai Soh, Guang-Bin Huang · 2009 · Neurocomputing
100. Model averaging prediction by K-fold cross-validation
     Xinyu Zhang, Chu-An Liu · 2022 · Journal of Econometrics