Bagging predictors

Cited by (1,119)

Items 901–1,000 of 1,119

1. Roughly balanced bagging for imbalanced data
   Shohei Hido, Hisashi Kashima, Yutaka Takahashi · 2009 · Statistical Analysis and Data Mining The ASA Data Science Journal
2. A new nested ensemble technique for automated diagnosis of breast cancer
   Moloud Abdar, Mariam Zomorodi Moghadam, Xujuan Zhou, Raj Gururajan, Xiaohui Tao, Prabal Datta Barua, Rashmi Gururajan · 2018 · Pattern Recognition Letters
3. A Review of Practical AI for Remote Sensing in Earth Sciences
   Bhargavi Janga, Gokul Prathin Asamani, Ziheng Sun, Nicoleta C. Cristea · 2023 · Remote Sensing
4. A quantitative uncertainty metric controls error in neural network-driven chemical discovery
   Janet, Jon Paul, Duan, Chenru, Yang, Tzuhsiung, Nandy, Aditya, Kulik, Heather J. · 2019 · Chemical Science
5. Decision Tree and Ensemble Learning Algorithms with Their Applications in Bioinformatics
   Dongsheng Che, Qi Liu, Khaled Rasheed, Xiuping Tao · 2011 · Advances in experimental medicine and biology
6. A comparative assessment of classification methods
   Melody Yihwa Kiang · 2003 · Decision Support Systems
7. Machine learning ensembles for wind power prediction
   Justin Heinermann, Oliver Krämer · 2015 · Renewable Energy
8. A Voting Ensemble Classifier for Wafer Map Defect Patterns Identification in Semiconductor Manufacturing
   Muhammad Saqlain, Bilguun Jargalsaikhan, Jong Yun Lee · 2019 · IEEE Transactions on Semiconductor Manufacturing
9. Data quantity governance for machine learning in materials science
   Yue Liu, Zhengwei Yang, Xinxin Zou, Shuchang Ma, Dahui Liu, Maxim Avdeev, Siqi Shi · 2023 · National Science Review
10. Combinations of biomarkers predictive of later life mortality
    Gruenewald, Tara L., Seeman, Teresa E., Ryff, Carol D., Karlamangla, Arun S., Singer, Burton H. · 2006 · Proceedings of the National Academy of Sciences
11. Decision tree classifiers for automated medical diagnosis
    Azar, Ahmad Taher, El-Metwally, Shereen M. · 2013 · Neural Computing and Applications
12. Combining time series models for forecasting
    Hui Zou, Yuhong Yang · 2003 · International Journal of Forecasting
13. Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series
    Charlotte Pelletier, Silvia Valero, Jordi Inglada, Nicolas Champion, Claire Marais-Sicre, Gérard Dedieu · 2017 · Remote Sensing
14. A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling
    Binh Thai Pham, Abolfazl Jaafari, Indra Prakash, Dieu Tien Bui · 2018 · Bulletin of Engineering Geology and the Environment
15. An Introduction to Bootstrap Methods with Applications to R
    Michael Ross Chernick, Robert A LaBudde · 2011 · ODU Digital Commons (Old Dominion University)
16. Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy
    Pablo Mesejo, Daniel Pizarro, Armand Abergel, Olivier Rouquette, Sylvain Béorchia, Laurent Poincloux, Adrien Bartoli · 2016 · IEEE Transactions on Medical Imaging
17. GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches
    Alireza Arabameri, Khalil Rezaei, Artemi Cerdà, Luigi Lombardo, Jesús Rodrigo‐Comino · 2018 · The Science of The Total Environment
18. Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition
    Mumtaz Ali, Ramendra Prasad · 2019 · Renewable and Sustainable Energy Reviews
19. Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: A comparative study
    Pin Zhang, Huai-Na Wu, Renpeng Chen, Tommy H.T. Chan · 2020 · Tunnelling and Underground Space Technology
20. Random Balance: Ensembles of variable priors classifiers for imbalanced data
    José-Francisco Díez-Pastor, Juan J. Rodríguez, César García‐Osorio, Ludmila Ilieva Kuncheva · 2015 · Knowledge-Based Systems
21. Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments
    Steven E. Sesnie, Paul E. Gessler, Bryan Finegan, Sirpa Thessler · 2008 · Remote Sensing of Environment
22. Women’s Pathways to Serious and Habitual Crime
    Tim Brennan, Markus Breitenbach, William A. Dieterich, Emily J. Salisbury, Patricia Van Voorhis · 2012 · Criminal Justice and Behavior
23. Rice diseases classification using feature selection and rule generation techniques
    Santanu Phadikar, Jaya Sil, Asit Kumar Das · 2012 · Computers and Electronics in Agriculture
24. Ranking in evolving complex networks
    Hao Liao, Manuel Sebastian Mariani, Matúš Medo, Yi‐Cheng Zhang, Ming-Yang Zhou · 2017 · Physics Reports
25. Identifying Pb-free perovskites for solar cells by machine learning
    Jino Im, Sungwon Lee, Tae-Wook Ko, Hyun Woo Kim, YunKyong Hyon, Hyunju Chang · 2019 · npj Computational Materials
26. Enriched random forests
    Dhammika Amaratunga, Javier Cabrera, Yung-Seop Lee · 2008 · Bioinformatics
27. Prosthesis size and long-term survival after aortic valve replacement
    Eugene H. Blackstone, Delos M. Cosgrove, W.R. Eric Jamieson, Nancy J. O. Birkmeyer, John H. Lemmer, David Craig Miller, Eric G. Butchart, Giulio Rizzoli, Magdi Habib Yacoub, Akiko Chai · 2003 · Journal of Thoracic and Cardiovascular Surgery
28. Using the Gene Ontology for Microarray Data Mining: A Comparison of Methods and Application to Age Effects in Human Prefrontal Cortex
    Paul Pavlidis, Jie Qin, Victoria Arango, John J. Mann, Etienne L. Sibille · 2004 · Neurochemical Research
29. Early segmentation of students according to their academic performance: A predictive modelling approach
    Vera Lucia Miguéis, Ana Freitas, Paulo J V Garcia, André Silva · 2018 · Decision Support Systems
30. Self-paced Ensemble for Highly Imbalanced Massive Data Classification
    Zhining Liu, Wei Cao, Zhifeng Gao, Jiang Bian, Hechang Chen, Yi Chang, Tie-Yan Liu · 2020 · IEEE International Conference on Data Engineering (ICDE)
31. The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system
    Mustafa Cemal Cakir, Mehmet Ali Güvenç, Selçuk Mıstıkoğlu · 2020 · Computers & Industrial Engineering
32. Active Learning with Multiple Views
    Ion Muslea, Steven Minton, Craig A. Knoblock · 2006 · Journal of Artificial Intelligence Research
33. Urban growth models: progress and perspective
    Xuecao Li, Peng Gong · 2016 · 中国科学通报：英文版
34. A decision support system for automated identification of sleep stages from single-channel EEG signals
    Ahnaf Rashik Hassan, Abdülhamit Subaşı · 2017 · Knowledge-Based Systems
35. Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping
    Prima Riza Kadavi, Chang-Wook Lee, Saro Lee · 2018 · Remote Sensing
36. Ensemble Learning
    Zhi‐Hua Zhou · 2021 · Springer eBooks
37. Evolutionary bagging for ensemble learning
    Giang Huong Ngo, Rodney R. Beard, Rohitash Chandra · 2022 · Neurocomputing
38. An application of Random Forests to a genome-wide association dataset: Methodological considerations & new findings
    Benjamin Alan Goldstein, Alan Hubbard, Adele Cutler, Lisa F. Barcellos · 2010 · BMC Genetics
39. Finding Needles in a Haystack: Using Data Analytics to Improve Fraud Prediction
    Johan L. Perols, Robert M. Bowen, Carsten Zimmermann, Basamba Samba · 2016 · The Accounting Review
40. Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean
    Mohsen Yoosefzadeh-Najafabadi, Hugh James Earl, Dan Tulpan, John J. Sulik, Milad Eskandari · 2021 · Frontiers in Plant Science
41. Better GP benchmarks: community survey results and proposals
    David R. White, James McDermott, Mauro Castelli, Luca Manzoni, Brian W. Goldman, Gabriel Kronberger, Wojciech Jaśkowski, Una-May O’Reilly, Sean Luke · 2012 · Genetic Programming and Evolvable Machines
42. How Useful Is Bagging in Forecasting Economic Time Series? A Case Study of U.S. Consumer Price Inflation
    Atsushi Inoue, Lutz Kilian · 2008 · Journal of the American Statistical Association
43. A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry
    Kristof Coussement, Stefan Lessmann, Geert Verstraeten · 2016 · Decision Support Systems
44. Optimizing ensemble weights and hyperparameters of machine learning models for regression problems
    Mohsen Shahhosseini, Guiping Hu, Hieu Pham · 2022 · Machine Learning with Applications
45. Deep co-training for semi-supervised image segmentation
    Jizong Peng, Guillermo Estrada, Marco Pedersoli, Christian Desrosiers · 2020 · Pattern Recognition
46. On Oblique Random Forests
    Bjoern H. Menze, B. Michael Kelm, Daniel Nicolas Splitthoff, Ullrich Köthe, Fred A. Hamprecht · 2011 · Lecture notes in computer science
47. Quantifying changes in the rates of forest clearing in Indonesia from 1990 to 2005 using remotely sensed data sets
    Matthew C. Hansen, Stephen V. Stehman, Peter V. Potapov, Belinda Arunarwati Margono, Fred Stolle, Kyle W. Pittman · 2009 · Environmental Research Letters
48. A Survey of Content-Aware Video Analysis for Sports
    Huang-Chia Shih · 2017 · IEEE Transactions on Circuits and Systems for Video Technology
49. A Comparison of State‐of‐the‐Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection
    Stijn Viaene, Richard A. Derrig, Bart Baesens, Guido Dedene · 2002 · Journal of Risk & Insurance
50. Decision Forest: Combining the Predictions of Multiple Independent Decision Tree Models
    Weida Tong, Huixiao Hong, Hong Fang, Qian Xie, Roger Perkins · 2003 · Journal of Chemical Information and Computer Sciences
51. Improving the accuracy of rainfall rates from optical satellite sensors with machine learning — A random forests-based approach applied to MSG SEVIRI
    Meike Kühnlein, Tim Appelhans, Boris Thies, Thomas Nauß · 2013 · Remote Sensing of Environment
52. Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
    Hanan G. Ayad, Mohamed S. Kamel · 2007 · IEEE Transactions on Pattern Analysis and Machine Intelligence
53. Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression
    Durga Lal Shrestha, DIMITRI P. SOLOMATINE · 2006 · Neural Computation
54. Sum versus vote fusion in multiple classifier systems
    Josef Kittler, Fuad M. Alkoot · 2003 · IEEE Transactions on Pattern Analysis and Machine Intelligence
55. Multiple Kernel Learning for Hyperspectral Image Classification: A Review
    Yanfeng Gu, Jocelyn Chanussot, Xiuping Jia, Jón Atli Benediktsson · 2017 · IEEE Transactions on Geoscience and Remote Sensing
56. Machine learning methods applied to drilling rate of penetration prediction and optimization - A review
    Luís Felipe Ferreira Motta Barbosa, Andreas Nascimento, Mauro Hugo Mathias, João Andrade de Carvalho · 2019 · Journal of Petroleum Science and Engineering
57. Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning
    Gideon A. Lyngdoh, Mohd Faiz Mohammad Zaki, N. M. Anoop Krishnan, Sumanta Das · 2022 · Cement and Concrete Composites
58. Random vector functional link network: Recent developments, applications, and future directions
    A. K. Malik, Ruobin Gao, M. A. Ganaie, M. Tanveer, Ponnuthurai Nagaratnam Suganthan · 2023 · Applied Soft Computing
59. FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
    Hong-You Chen, Wei‐Lun Chao · 2020 · arXiv (Cornell University)
60. Should lung transplantation be performed for patients on mechanical respiratory support? The US experience
    Mason, David P., Thuita, Lucy, Nowicki, Edward R., Murthy, Sudish C., Pettersson, Gösta B., Blackstone, Eugene H. · 2010 · The Journal of Thoracic and Cardiovascular Surgery
61. A random forest classifier for lymph diseases
    Ahmad Taher Azar, Hanaa Ismail Elshazly, Aboul Ella Hassanien, Abeer ElKorany · 2013 · Computer Methods and Programs in Biomedicine
62. Boosting Decision Trees
    Harris Drucker, Corinna Cortes · 1995 · neural information processing systems
63. Trainable classifier-fusion schemes: An application to pedestrian detection
    Oswaldo Ludwig, David Delgado, Valter Gonçalves, Urbano Nunes · 2009 · IEEE International Conference on Intelligent Transportation Systems (ITSC)
64. Morbidity of Bleeding After Cardiac Surgery: Is It Blood Transfusion, Reoperation for Bleeding, or Both?
    Alessandro Vivacqua, Colleen Gorman Koch, Arshad M. Yousuf, Edward R. Nowicki, Penny L. Houghtaling, Eugene H. Blackstone, Joseph F. Sabik · 2011 · The Annals of Thoracic Surgery
65. Correlation and association analyses in microbiome study integrating multiomics in health and disease
    Yinglin Xia · 2020 · Progress in molecular biology and translational science
66. Deep learning forecast of rainfall-induced shallow landslides
    Alessandro Cesare Mondini, Fausto Guzzetti, Massimo Melillo · 2023 · Nature Communications
67. Combining Linear Regression Models
    Zheng Yuan, Yuhong Yang · 2005 · Journal of the American Statistical Association
68. Large-scale machine learning at twitter
    Jimmy Lin, Alek Kolcz · 2012 · ACM SIGMOD International Conference on Management of Data (SIGMOD)
69. EnsembleMatrix
    Justin Talbot, Bongshin Lee, Ashish Kapoor, Desney S. Tan · 2009 · CHI Conference on Human Factors in Computing Systems
70. Random Subspace Ensembles for fMRI Classification
    Ludmila Ilieva Kuncheva, Juan J. Rodríguez, Catrin Plumpton, David E.J. Linden, Stephen J. Johnston · 2010 · IEEE Transactions on Medical Imaging
71. Machine Learning Methods for Demand Estimation
    Patrick Bajari, Denis Nekipelov C, Stephen Ryan, Miaoyu Yang · 2015 · American Economic Review
72. Analyzing PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measures
    Wendy S. Yambor, Bruce A. Draper, J. Ross Beveridge · 2002 · Series in machine perception and artificial intelligence
73. Fast SVM training algorithm with decomposition on very large data sets
    Jian-xiong Dong, Krzyzak, A., Suen, C.Y. · 2005 · IEEE Transactions on Pattern Analysis and Machine Intelligence
74. High-Dimensional Inference: Confidence Intervals, $p$-Values and R-Software hdi
    Ruben Dezeure, Peter Bühlmann, Lukas Meier, Nicolai Meinshausen · 2015 · Statistical Science
75. A New Feature Transformation Method Based on Rotation for Speaker Identification
    Minseok Kim, Ha-Jin Yu · 2007 · IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
76. Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating
    Ahnaf Rashik Hassan, Siuly Siuly, Yanchun Zhang · 2016 · Computer Methods and Programs in Biomedicine
77. Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake
    Austin J. Cooner, Yang Shao, James B. Campbell · 2016 · Remote Sensing
78. Local discriminant bases and their applications
    Naoki Saito, Ronald R. Coifman · 1995 · Journal of Mathematical Imaging and Vision
79. Bias-corrected random forests in regression
    Zhang, Guoyi, Lu, Yan · 2012 · Journal of Applied Statistics
80. Identifying pollution sources and predicting urban air quality using ensemble learning methods
    Kunwar Pal Singh, Shikha Pranesh Gupta, Premanjali Rai · 2013 · Atmospheric Environment
81. Patient and Surgical Factors Influencing Air Leak After Lung Volume Reduction Surgery: Lessons Learned From the National Emphysema Treatment Trial
    Malcolm M. DeCamp, Eugene H. Blackstone, Keith S. Naunheim, Mark J. Krasna, Douglas E. Wood, Yvonne M. Meli, Robert Joseph McKenna · 2006 · The Annals of Thoracic Surgery
82. Statistical Learning Methods
    Franck Vermet · 2018 · Wiley eBooks
83. Committee neural network potentials control generalization errors and enable active learning
    Christoph Schran, Kryštof Březina, Ondřej Maršálek · 2020 · The Journal of Chemical Physics
84. LibD3C: Ensemble classifiers with a clustering and dynamic selection strategy
    Chen Lin, Wenqiang Chen, Cheng Qiu, Yunfeng Wu, Sridhar Krishnan, Quan Zou · 2013 · Neurocomputing
85. Cannulation of the axillary artery with a side graft reduces morbidity
    Joseph F. Sabik, Hassan Nemeh, Bruce Whitney Lytle, Eugene H. Blackstone, Alan Marc Gillinov, Jeevanantham Rajeswaran, Delos M. Cosgrove · 2004 · The Annals of Thoracic Surgery
86. Mixture of experts for classification of gender, ethnic origin, and pose of human faces
    Srinivas Gutta, Jeffrey R. Huang, P. Jonathon, Harry Wechsler · 2000 · IEEE Transactions on Neural Networks
87. Cooperative Coevolution of Artificial Neural Network Ensembles for Pattern Classification
    Nicolás García‐Pedrajas, César Hervás‐Martínez, Domingo Ortíz-Boyer · 2005 · IEEE Transactions on Evolutionary Computation
88. Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments
    Marjan Čeh, Milan Kilibarda, Anka Lisec, Branislav Bajat · 2018 · ISPRS International Journal of Geo-Information
89. Review of Classifier Combination Methods
    Sergey Tulyakov, Stefan Jaeger, Venu Govindaraju, David Doermann · 2007 · Studies in computational intelligence
90. Recent Developments in Bootstrap Methodology
    Anthony C. Davison, DAVID V. HINKLEY, G. ALASTAIR YOUNG · 2003 · Statistical Science
91. Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis
    Alexandra Balahur, Marco Turchi · 2013 · Computer Speech & Language
92. Spectral Clustering Ensemble Applied to SAR Image Segmentation
    Xiangrong Zhang, Licheng Jiao, Fang Liu, Liefeng Bo, Maoguo Gong · 2008 · IEEE Transactions on Geoscience and Remote Sensing
93. Random survival forests for high‐dimensional data
    Hemant Ishwaran, Udaya B. Kogalur, Xi Chen, Andy J. Minn · 2011 · Statistical Analysis and Data Mining The ASA Data Science Journal
94. Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective
    John Kang, Russell S. Schwartz, John C. Flíckinger, Sushil Beriwal · 2015 · International Journal of Radiation Oncology*Biology*Physics
95. Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble
    Haoyuan Hong, Junzhi Liu, A‐Xing Zhu · 2020 · The Science of The Total Environment
96. Casting out Demons: Sanitizing Training Data for Anomaly Sensors
    Cretu, Gabriela F., Stavrou, Angelos, Locasto, Michael E., Stolfo, Salvatore J., Keromytis, Angelos D. · 2008 · 2008 IEEE Symposium on Security and Privacy (sp 2008)
97. Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
    Bhatt, Umang, Antorán, Javier, Zhang, Yunfeng, Liao, Q. Vera, Sattigeri, Prasanna, Fogliato, Riccardo, Melançon, Gabrielle, Krishnan, Ranganath, Stanley, Jason, Tickoo, Omesh et al. · 2021 · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society
98. Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit
    Camps, Julià, Samà, Albert, Martín, Mario, Rodríguez-Martín, Daniel, Pérez-López, Carlos, Moreno Arostegui, Joan M., Cabestany, Joan, Català, Andreu, Alcaine, Sheila, Mestre, Berta et al. · 2018 · Knowledge-Based Systems
99. Authorship Attribution for Social Media Forensics
    Rocha, Anderson, Scheirer, Walter J., Forstall, Christopher W., Cavalcante, Thiago, Theophilo, Antonio, Shen, Bingyu, Carvalho, Ariadne R. B., Stamatatos, Efstathios · 2017 · IEEE Transactions on Information Forensics and Security
100. Aortic root replacement with cryopreserved allograft for prosthetic valve endocarditis
     Sabik, Joseph F., Lytle, Bruce W., Blackstone, Eugene H., Marullo, Antonino G.M., Pettersson, Gosta B., Cosgrove, Delos M. · 2002 · The Annals of Thoracic Surgery