Bagging predictors

Cited by (1,119)

Items 401–500 of 1,119

1. Berlin Heart EXCOR Pediatric Ventricular Assist Device for Bridge to Heart Transplantation in US Children
   Christopher S.D. Almond, David L.S. Morales, Eugene H. Blackstone, Mark W. Turrentine, Michiaki Imamura, Mary Patricia Massicotte, Lori C. Jordan, Eric J. Devaney, Chitra Ravishankar, Kirk R. Kanter et al. · 2013 · Circulation
2. Statistical Issues in cDNA Microarray Data Analysis
   Gordon K. Smyth, Jean Yee Hwa Yang, Terence Paul Speed · 2003 · Humana Press eBooks
3. Defect prediction from static code features: current results, limitations, new approaches
   Tim Menzies, Zach Milton, Burak Turhan, Bojan Čukić, Yue Jiang, Ayşe Bener · 2010 · Automated Software Engineering
4. Random forest-based similarity measures for multi-modal classification of Alzheimer's disease
   Katherine R. Gray, Paul Aljabar, Rolf A. Heckemann, Alexander Hammers, Daniel Rueckert · 2012 · NeuroImage
5. Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models
   Xilei Zhao, Xiang “Jacob” Yan, Alan Yu, Pascal Van Hentenryck · 2020 · Travel Behaviour and Society
6. 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network
   Hongming Shan, Yi Zhang, Qingsong Yang, Uwe Krüger, Mannudeep K. Kalra, Ling Sun, Wenxiang Cong, Ge Wang · 2018 · IEEE Transactions on Medical Imaging
7. Learning and making decisions when costs and probabilities are both unknown
   Bianca Zadrozny, Charles Elkan · 2001 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
8. Dynamic classifier selection: Recent advances and perspectives
   Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti · 2017 · Information Fusion
9. A Comparison of Decision Tree Ensemble Creation Techniques
   Robert E. Banfield, Lawrence Hall, Kevin W. Bowyer, W. Philip Kegelmeyer · 2006 · IEEE Transactions on Pattern Analysis and Machine Intelligence
10. A deep learning approach for ECG-based heartbeat classification for arrhythmia detection
    Giovanna Sannino, Giuseppe De Pietro · 2018 · Future Generation Computer Systems
11. Machine learning in energy economics and finance: A review
    Hamed Ghoddusi, Germán G. Creamer, Nima Rafizadeh · 2019 · Energy Economics
12. Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter
    Kyumin Lee, Brian David Eoff, James Caverlee · 2021 · Proceedings of the International AAAI Conference on Web and Social Media
13. Empirical Margin Distributions and Bounding the Generalization Error of Combined Classifiers
    Vladimir I. Koltchinskii, Dmitry Panchenko · 2002 · The Annals of Statistics
14. An Introduction to Boosting and Leveraging
    Ron Meir, Gunnar Rätsch · 2003 · Lecture notes in computer science
15. The Role of Occam's Razor in Knowledge Discovery
    Domingos, Pedro · 1999 · Data Mining and Knowledge Discovery
16. A Survey of Decision Trees: Concepts, Algorithms, and Applications
    Ibomoiye Domor Mienye, Nobert Rangarirai Jere · 2024 · IEEE Access
17. Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms
    Johan L. Perols · 2011 · Auditing A Journal of Practice & Theory
18. Consistency of Random Forests and Other Averaging Classifiers
    Gérard Biau A, Luc Devroye, Gábor Lugosi · 2007
19. Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting
    Xueheng Qiu, Ye Ren, Ponnuthurai Nagaratnam Suganthan, Gehan A. J. Amaratunga · 2017 · Applied Soft Computing
20. cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R
    Christian Thiele, Gerrit H. F. Hirschfeld · 2021 · Journal of Statistical Software
21. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information
    Jock A. Blackard, Mark V. Finco, Eileen H. Helmer, Geoffrey R. Holden, Michael L. Hoppus, Dennis M. Jacobs, Andrew J. Lister, Gretchen G. Moisen, M NELSON, Rachel I. Riemann · 2007 · Remote Sensing of Environment
22. Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique
    Tuan V. Vu, Zongbo Shi, Jing Cheng, Qiang Zhang, Kebin He, Shuxiao Wang, Roy M. Harrison · 2019 · Atmospheric chemistry and physics
23. Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy
    Jens B. Stephansen, Alexander Neergaard Olesen, Mads Olsen, Aditya Ambati, Eileen B. Leary, Hyatt E. Moore, Oscar Carrillo, Ling Lin, Fang Han, Han Yan et al. · 2018 · Nature Communications
24. Coupling machine learning and crop modeling improves crop yield prediction in the US corn belt
    Shahhosseini, Mohsen, Hu, Guiping, Huber, Isaiah, Archontoulis, Sotirios V. · 2021 · Scientific Reports
25. CBSA: content-based soft annotation for multimodal image retrieval using bayes point machines
    E. Chang, King-Shy Goh, G. Sychay, Gang Wu · 2003 · IEEE Transactions on Circuits and Systems for Video Technology
26. Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting
    Wouter Buckinx, Dirk Van den Poel · 2004 · European Journal of Operational Research
27. The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
    Leandro Lei Minku, A. P. White, Xin Yao · 2009 · IEEE Transactions on Knowledge and Data Engineering
28. A review of machine learning in processing remote sensing data for mineral exploration
    Hojat Shirmard, Ehsan Farahbakhsh, R. Dietmar Müller, Rohitash Chandra · 2021 · Remote Sensing of Environment
29. Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction
    Maciej Zięba, Sebastian Klaudiusz Tomczak, Jakub M. Tomczak · 2016 · Expert Systems with Applications
30. A support vector machine-based ensemble algorithm for breast cancer diagnosis
    Haifeng Wang, Bichen Zheng, Sang Won Yoon, Hoo Sang Ko · 2017 · European Journal of Operational Research
31. AST: Audio Spectrogram Transformer
    Yuan Gong, Yu-An Chung, James Robert Glass · 2021 · arXiv (Cornell University)
32. Data fusion in intelligent transportation systems: Progress and challenges – A survey
    Faouzi, Nour-Eddin El, Leung, Henry, Kurian, Ajeesh · 2011 · Information Fusion
33. Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods
    Pınar Tüfekçi · 2014 · International Journal of Electrical Power & Energy Systems
34. The Evolution of Boosting Algorithms
    Harald Binder, Olaf Gefeller, Matthias C. Schmid, Andreas J. Mayr · 2014 · Methods of Information in Medicine
35. Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM+ data
    Peter V. Potapov, Svetlana A. Turubanova, Matthew C. Hansen, Bernard Adusei, Mark Broich, Alice Altstatt, Landing Mané, Christopher O. Justice · 2012 · Remote Sensing of Environment
36. Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT
    Vojtěch Holub, Jessica Fridrich · 2014 · IEEE Transactions on Information Forensics and Security
37. T-Finder: A Recommender System for Finding Passengers and Vacant Taxis
    Nicholas Jing Yuan, Yu Zheng, Liuhang Zhang, Xing Xie · 2012 · IEEE Transactions on Knowledge and Data Engineering
38. Machine learning for identifying Randomized Controlled Trials: An evaluation and practitioner's guide
    Marshall, Iain J., Noel‐Storr, Anna, Kuiper, Joël, Thomas, James, Wallace, Byron C. · 2018 · Research Synthesis Methods
39. A deep look into radiomics
    Scapicchio, Camilla, Gabelloni, Michela, Barucci, Andrea, Cioni, Dania, Saba, Luca, Neri, Emanuele · 2021 · La radiologia medica
40. Ensemble methods for wind and solar power forecasting—A state-of-the-art review
    Ren, Ye, Suganthan, P.N., Srikanth, N. · 2015 · Renewable and Sustainable Energy Reviews
41. Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods
    Erick Meira, Fernando Luiz Cyrino Oliveira · 2017 · Energy
42. Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods
    Marcelo Cunha Medeiros, Gabriel Vasconcelos, Álvaro Veiga, Eduardo Zilberman · 2019 · Journal of Business and Economic Statistics
43. Brain tumor detection using fusion of hand crafted and deep learning features
    Saba, Tanzila, Sameh Mohamed, Ahmed, El-Affendi, Mohammad, Amin, Javeria, Sharif, Muhammad · 2020 · Cognitive Systems Research
44. Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging
    Emilie Gérardin, Gaël Chételat, Marie Chupin A, Rémi Cuingnet, Béatrice Desgranges, Hosung Kim, Marc Niethammer, Bruno Dubois, Stéphane Lehéricy, Line Garnero et al. · 2009 · NeuroImage
45. A review on machine learning principles for multi-view biological data integration
    Yifeng Li, Fang‐Xiang Wu, Alioune Ngom · 2016 · Briefings in Bioinformatics
46. Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection
    Igor V. Tetko, Iurii Sushko, Anil Kumar Pandey, Hao Zhu, Alexander Tropsha, Ester Papa, Tomas Öberg, Roberto Todeschini, Denis Fourches, Alexandre A. Varnek · 2008 · Journal of Chemical Information and Modeling
47. Evaluation of gaussian processes and other methods for non-linear regression
    Geoffrey E. Hinton, Carl Edward Rasmussen · 1997 · Library and Archives Canada (Government of Canada)
48. Robust fine-tuning of zero-shot models
    Wortsman, Mitchell, Ilharco, Gabriel, Kim, Jong Wook, Li, Mike, Kornblith, Simon, Roelofs, Rebecca, Lopes, Raphael Gontijo, Hajishirzi, Hannaneh, Farhadi, Ali, Namkoong, Hongseok et al. · 2022 · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
49. Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree
    Vladimir Vava Gligorov, Mike Williams · 2013 · Journal of Instrumentation
50. Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery
    Congcong Li, Jie Wang, Lei Wang, Luanyun Hu, Peng Gong · 2014 · Remote Sensing
51. HIBAG—HLA genotype imputation with attribute bagging
    Xiuwen Zheng, Judong Shen, Charles J. Cox, Jon C. Wakefield, Margaret G. Ehm, Matthew R. Nelson, Bruce S Weir · 2013 · The Pharmacogenomics Journal
52. Boosted Decision Tree Analysis of Surface-enhanced Laser Desorption/Ionization Mass Spectral Serum Profiles Discriminates Prostate Cancer from Noncancer Patients
    Yinsheng Qu, Bao-Ling Adam, Yutaka Yasui, Michael D Ward, Lisa H. Cazares, Paul F. Schellhammer, Ziding Feng, Oliver John Semmes, George L. Wright · 2002 · Clinical Chemistry
53. Fake news detection within online social media using supervised artificial intelligence algorithms
    Feyza Altunbey Ozbay, Bilal Alataş · 2019 · Physica A Statistical Mechanics and its Applications
54. A comparative study of machine learning classifiers for modeling travel mode choice
    Hagenauer, Julian, Helbich, Marco · 2017 · Expert Systems with Applications
55. Dynamic weighted majority: a new ensemble method for tracking concept drift
    J. Zico Kolter, Marcus A. Maloof · 2004 · Proceedings
56. A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification
    Ce Zhang, Xin Pan, Huapeng Li, Andy Gardiner, Isabel M. J. Sargent, Jonathon S. Hare, Peter M. Atkinson · 2017 · ISPRS Journal of Photogrammetry and Remote Sensing
57. Semi-supervised learning by disagreement
    Zhi‐Hua Zhou, Ming Li · 2009 · Knowledge and Information Systems
58. EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
    Mikel Galar, Alberto Fernández, Edurne Barrenechea, Francisco Herrera · 2013 · Pattern Recognition
59. Quantile regression neural networks: Implementation in R and application to precipitation downscaling
    Alex J. Cannon · 2010 · Computers & Geosciences
60. Loudness predicts prominence: Fundamental frequency lends little
    Greg Kochanski, Esther Grabe, John Coleman, B. Rosner · 2005 · The Journal of the Acoustical Society of America
61. To Combat Multi-Class Imbalanced Problems by Means of Over-Sampling Techniques
    Lida Abdi, Sattar Hashemi · 2015 · IEEE Transactions on Knowledge and Data Engineering
62. Multi-target regression via input space expansion: treating targets as inputs
    Eleftherios Spyromitros-Xioufis, Grigorios Tsoumakas, William Groves, Ioannis P. Vlahavas · 2016 · Machine Learning
63. A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm
    Qinghua Han, Changqing Gui, Jie Xu, Giuseppe Lacidogna · 2019 · Construction and Building Materials
64. CatBoost model and artificial intelligence techniques for corporate failure prediction
    Sami Ben Jabeur, Cheima Gharib, Salma Mefteh‐Wali, Wissal Ben Arfi · 2021 · Technological Forecasting and Social Change
65. A novel ensemble method for classifying imbalanced data
    Zhongbin Sun, Qinbao Song, Xiaoyan Zhu, Heli Sun, Baowen Xu, Yuming Zhou · 2014 · Pattern Recognition
66. Modeling Species Distribution and Change Using Random Forest
    Jeffrey S. Evans, Melanie A. Murphy, Zachary A. Holden, Samuel A. Cushman · 2010 · Springer eBooks
67. The Practical Implementation of Bayesian Model Selection
    Chipman, Hugh, George, Edward I., McCulloch, Robert E. · 2001 · Institute of Mathematical Statistics Lecture Notes - Monograph Series
68. Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks
    Santos, Claudio Filipi Gonçalves Dos, Papa, João Paulo · 2022 · ACM Computing Surveys
69. Clipper: A Low-Latency Online Prediction Serving System
    Crankshaw, Daniel, Wang, Xin, Zhou, Giulio, Franklin, Michael J., Gonzalez, Joseph E., Stoica, Ion · 2016 · arXiv
70. Leveraging Bagging for Evolving Data Streams
    Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer · 2010 · Lecture notes in computer science
71. Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach
    Aytuğ Onan · 2020 · Computer Applications in Engineering Education
72. An analysis of diversity measures
    E. K. Tang, Ponnuthurai Nagaratnam Suganthan, Xin Yao · 2006 · Machine Learning
73. Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components
    L. Benali, Gilles Notton, Alexis Fouilloy, Cyril Voyant, Rabah Dizène · 2018 · Renewable Energy
74. Bagging and Boosting Classification Trees to Predict Churn
    Lemmens, Aurélie, Croux, Christophe · 2006 · Journal of Marketing Research
75. Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach
    Sara Hosseinzadeh Kassani, Peyman Hosseinzadeh Kassani, Michal J. Wesolowskic, Kevin A. Schneidera, Ralph Detersa · 2021 · Journal of Applied Biomedicine
76. A Review of Android Malware Detection Approaches Based on Machine Learning
    Kaijun Liu, Shengwei Xu, Guoai Xu, Miao Zhang, Dawei Sun, Haifeng Liu · 2020 · IEEE Access
77. A systematic literature review of methods and datasets for anomaly-based network intrusion detection
    Zhen Yang, Xiaodong Liu, Tong Li, Di Wu, Jinjiang Wang, Yunwei Zhao, Han Han · 2022 · Computers & Security
78. Dimensionality Reduction via Sparse Support Vector Machines
    Jinbo Bi, Kristin P. Bennett, Mark J. Embrechts, Curt M. Breneman, Minghu Song, Isabelle Guyon, André Elisseeff · 2003
79. Deep Learning for Medical Image-Based Cancer Diagnosis
    Xiaoyan Jiang, Zuojin Hu, Shuihua Wang‎, Yudong Zhang · 2023 · Cancers
80. Bolasso
    Francis Bach · 2008 · International Conference on Machine Learning
81. Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions
    Henry Friday Nweke, Teh Ying Wah, Ghulam Mujtaba, Mohammed Ali Al-Garadi · 2018 · Information Fusion
82. TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System
    Bayu Adhi Tama, Marco Comuzzi, Kyung-Hyune Rhee · 2019 · IEEE Access
83. Random Forest Spatial Interpolation
    Aleksandar Sekulić, Milan Kilibarda, G.B.M. Heuvelink, Mladen Nikolić, Branislav Bajat · 2020 · Remote Sensing
84. Cluster analysis applied to regional geochemical data: Problems and possibilities
    Matthias Templ, Peter Filzmoser A, Clemens Reimann C · 2008 · Applied Geochemistry
85. Simple decision rules for classifying human cancers from gene expression profiles
    Aik Choon Tan, Daniel Q. Naiman, Lei Xu, Raimond L. Winslow, Donald Geman · 2005 · Computer applications in the biosciences
86. Application of machine learning methods to spatial interpolation of environmental variables
    Jin Li, Andrew D. Heap, Anna Potter, James J. Daniell · 2011 · Environmental Modelling & Software
87. A Survey on ensemble learning under the era of deep learning
    Yongquan Yang, Haijun Lv, Ning Chen · 2022 · Artificial Intelligence Review
88. Inferring transportation modes from GPS trajectories using a convolutional neural network
    Dabiri, Sina, Heaslip, Kevin · 2018 · Transportation Research Part C: Emerging Technologies
89. Reliable ABC model choice via random forests
    Pierre Pudlo, Jean‐Michel Marin, Arnaud Estoup, Jean‐Marie Cornuet, Mathieu Gautier, Christian P. Robert · 2015 · Bioinformatics
90. Ensemble machine learning on gene expression data for cancer classification.
    Aik Choon Tan, David Gilbert · 2003 · PubMed
91. Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods – A comparison
    Jochem Verrelst, Juan Pablo Rivera-Caicedo, Frank Veroustraete, Jordi Muñoz-Marı́, Jan G.P.W. Clevers, Gustau Camps‐Valls, J. Moreno · 2015 · ISPRS Journal of Photogrammetry and Remote Sensing
92. Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey
    Buhrmester, Vanessa, Münch, David, Arens, Michael · 2021 · Machine Learning and Knowledge Extraction
93. An Empirical Study of Learning from Imbalanced Data Using Random Forest
    Taghi M. Khoshgoftaar, Moiz Golawala, Jason Van Hulse · 2007 · IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
94. An extensive experimental survey of regression methods
    Manuel Fernández-Delgado, Manisha Sanjay Sirsat, Eva Cernadas, Sadi Alawadi, Senén Barro, Manuel Febrero–Bande · 2018 · Neural Networks
95. A K -fold averaging cross-validation procedure
    Jung, Yoonsuh, Hu, Jianhua · 2015 · Journal of Nonparametric Statistics
96. Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines)
    Emmanuel John M. Carranza, Alice G. Laborte · 2014 · Computers & Geosciences
97. AdaBoost with SVM-based component classifiers
    Xuchun Li, Lei Wang, Eric Sung · 2007 · Engineering Applications of Artificial Intelligence
98. Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping
    Yanli Wu, Yutian Ke, Zhuo Chen, Shouyun Liang, Hongliang Zhao, Haoyuan Hong · 2019 · CATENA
99. Comparing automated text classification methods
    Hartmann, Jochen, Huppertz, Juliana, Schamp, Christina, Heitmann, Mark · 2019 · International Journal of Research in Marketing
100. The interaction of the explicit and the implicit in skill learning: a dual-process approach
     Sun, Ron, Slusarz, Paul, Terry, Chris · 2005 · Psychological Review