Bagging predictors

Cited by (1,119)

Items 601–700 of 1,119

1. The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial
   Benyamin Ghojogh, Mark Crowley · 2019 · arXiv (Cornell University)
2. Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles
   Lines, Jason, Taylor, Sarah, Bagnall, Anthony · 2018 · ACM Transactions on Knowledge Discovery from Data
3. A survey of regularization strategies for deep models
   Reza Moradi, Reza Berangi, Behrouz Minaei Bidgoli · 2019 · Artificial Intelligence Review
4. Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin
   Andrew Mellor, Samia Boukir, Andrew Haywood, Simon Jones · 2015 · ISPRS Journal of Photogrammetry and Remote Sensing
5. Multistrategy ensemble learning: reducing error by combining ensemble learning techniques
   Geoffrey I. Webb, Zhiming Zheng · 2004 · IEEE Transactions on Knowledge and Data Engineering
6. Machine Learning in Financial Crisis Prediction: A Survey
   Wei-Yang Lin, Ya‐Han Hu, Chih‐Fong Tsai · 2011 · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
7. Classifier ensembles for land cover mapping using multitemporal SAR imagery
   Björn Waske, Matthias Braun · 2009 · ISPRS Journal of Photogrammetry and Remote Sensing
8. Using Iterated Bagging to Debias Regressions
   Leo Breiman · 2001 · Machine Learning
9. Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online
   Felix Greaves, Daniel Ramirez-Cano, Christopher J. Millett, Ara Wardkes Darzi, Liam Donaldson · 2013 · Journal of Medical Internet Research
10. Towards dropout training for convolutional neural networks
    Haibing Wu, Xiaodong Gu · 2015 · Neural Networks
11. The influence of preprocessing on text classification using a bag-of-words representation
    Yaakov HaCohen‐Kerner, Daniel Miller, Yair Yigal · 2020 · PLoS ONE
12. Bao: Making Learned Query Optimization Practical
    Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, Tim Kraska · 2021 · ACM SIGMOD International Conference on Management of Data (SIGMOD)
13. Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)
    Jerome H. Friedman, Robert John Tibshirani, Trevor J. Hastie · 2000 · The Annals of Statistics
14. Artificial Intelligence Techniques for Stability Analysis and Control in Smart Grids: Methodologies, Applications, Challenges and Future Directions
    Shi, Zhongtuo, Yao, Wei, Li, Zhouping, Zeng, Lingkang, Zhao, Yifan, Zhang, Runfeng, Tang, Yong, Wen, Jinyu · 2020 · Applied Energy
15. Eastern Europe's forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive
    Peter V. Potapov, Svetlana A. Turubanova, Alexandra Tyukavina, А. М. Крылов, J. L. McCarty, Volker Christian Radeloff, Matthew C. Hansen · 2014 · Remote Sensing of Environment
16. Practical Confidence and Prediction Intervals
    Tom Heskes · 1996
17. GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks
    Dieu Tien Bui, Tien-Chung Ho, Biswajeet Pradhan, Binh Thai Pham, Viet‐Ha Nhu, Inge Revhaug · 2016 · Environmental Earth Sciences
18. Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar
    Miao Liu, Mingjun Wang, Jun Wang, Duo Li · 2012 · Sensors and Actuators B Chemical
19. Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)
    Lehmann, Christoph, Koenig, Thomas, Jelic, Vesna, Prichep, Leslie, John, Roy E., Wahlund, Lars-Olof, Dodge, Yadolah, Dierks, Thomas · 2007 · Journal of Neuroscience Methods
20. A review on design, modeling and applications of computer experiments
    Victoria C. P. Chen, Kwok‐Leung Tsui, Russell R. Barton, Martin Meckesheimer · 2006 · IIE Transactions
21. How can statistical models help to determine driving factors of landslides?
    Peter Vorpahl, Helmut Elsenbeer, Michael Maerker, Boris Schröder · 2012 · Ecological Modelling
22. Boosting Algorithms: A Review of Methods, Theory, and Applications
    Artur J. Ferreira, Mário A. T. Figueiredo · 2012 · Springer eBooks
23. Clinical experience with 202 adults receiving extracorporeal membrane oxygenation for cardiac failure: Survival at five years
    Nicholas Gerard Smedira, Nader Moazami, Camille M. Golding, Patrick M. McCarthy, Carolyn Apperson-Hansen, Eugene H. Blackstone, Delos M. Cosgrove · 2001 · Journal of Thoracic and Cardiovascular Surgery
24. Machine Learning in Materials Science
    Tim Mueller, Aaron Gilad Kusne, Rampi Ramprasad · 2016 · Reviews in computational chemistry
25. A survey on heuristic malware detection techniques
    Zahra Bazrafshan, Hashem Hashemi, Seyed Mehdi Hazrati Fard, Ali Hamzeh · 2013 · International Conference on Information and Knowledge Technology (IKT)
26. Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data
    Björn Waske, Sebastian van der Linden, Jón Atli Benediktsson, Andreas Rabe, Patrick Hostert · 2010 · IEEE Transactions on Geoscience and Remote Sensing
27. Class-Boundary Alignment for Imbalanced Dataset Learning
    Gang Wu, Edward Y. Chang · 2003
28. OUT-OF-BAG ESTIMATION
    Leo Breiman · 1996
29. Decoding brain states from fMRI connectivity graphs
    Jonas Richiardi, Hamdi Eryilmaz, Sophie Schwartz, Patrik O. Vuilleumier, Dimitri Van De Ville · 2010 · NeuroImage
30. To combine or not to combine: selecting among forecasts and their combinations
    Michèle Hibon, Theodoros Evgeniou · 2004 · International Journal of Forecasting
31. What Makes an Online Review More Helpful: An Interpretation Framework Using XGBoost and SHAP Values
    Meng Yuan, Nianhua Yang, Zhilin Qian, Gaoyu Zhang · 2020 · Journal of theoretical and applied electronic commerce research
32. Imbalanced Data Problem in Machine Learning: A Review
    Manahel Altalhan, Abdulmohsen Algarni, M. Turki-Hadj Alouane · 2025 · IEEE Access
33. An insight into machine-learning algorithms to model human-caused wildfire occurrence
    Marcos Rodrígues, Juan de la Riva · 2014 · Environmental Modelling & Software
34. Inverse random under sampling for class imbalance problem and its application to multi-label classification
    Muhammad Atif Tahir, Josef Kittler, Fei Yan · 2012 · Pattern Recognition
35. An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation
    Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez · 2009 · IEEE Transactions on Pattern Analysis and Machine Intelligence
36. The Long-term Efficacy of Pneumatic Dilatation and Heller Myotomy for the Treatment of Achalasia
    Marcelo F. Vela, Joel E. Richter, Farah Khandwala, Eugene H. Blackstone, Don M. Wachsberger, Mark E. Baker, Thomas W. Rice · 2006 · Clinical Gastroenterology and Hepatology
37. Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches
    Binh Thai Pham, Indra Prakash, Sushant Kumar Singh, Ataollah Shirzadi, Himan Shahabi, Thi-Thu-Trang Tran, Dieu Tien Bui · 2018 · CATENA
38. An Application of Oversampling, Undersampling, Bagging and Boosting in Handling Imbalanced Datasets
    Yap Bee Wah, Khatijahhusna Abd Rani, Hezlin Aryani Abd Rahman, Simon James Fong, Zuraida Khairudin, Nik Nik Abdullah · 2013 · Lecture notes in electrical engineering
39. Quantum Machine Learning: What Quantum Computing Means to Data Mining
    Péter Wittek · 2014
40. Classifier ensembles: Select real-world applications
    Nikunj C. Oza, Kagan Tumer · 2007 · Information Fusion
41. Combining Estiamates in Regression and Classification
    Michael Leo LeBlanc, Robert John Tibshirani · 1996 · Journal of the American Statistical Association
42. Iterative random forests to discover predictive and stable high-order interactions
    Basu, Sumanta, Kumbier, Karl, Brown, James B., Yu, Bin · 2018 · Proceedings of the National Academy of Sciences
43. Classifying earthquake damage to buildings using machine learning
    Sujith Mangalathu, Han Sun, Chukwuebuka Chukwuemeka Nweke, Zhengxiang Yi, Henry V. Burton · 2019 · Earthquake Spectra
44. Credit risk assessment with a multistage neural network ensemble learning approach
    L YU, Shouyang Wang, Kin Keung Lai · 2007 · Expert Systems with Applications
45. Multi-scale digital terrain analysis and feature selection for digital soil mapping
    Thorsten Behrens, A‐Xing Zhu, Karsten Schmidt, Thomas Scholten · 2009 · Geoderma
46. Risk factors for hospital morbidity and mortality after the Norwood procedure: A report from the Pediatric Heart Network Single Ventricle Reconstruction trial
    Sarah Tabbutt, Nancy S. Ghanayem, Chitra Ravishankar, Lynn A. Sleeper, David S. Cooper, Deborah U. Frank, Minmin Lü, Christian Pizarro, Peter C. Frommelt, Caren S. Goldberg et al. · 2012 · Journal of Thoracic and Cardiovascular Surgery
47. Ensemble feature selection: Homogeneous and heterogeneous approaches
    Borja Seijo-Pardo, Iago Porto-Díaz, Verónica Bolón‐Canedo, Amparo Alonso‐Betanzos · 2016 · Knowledge-Based Systems
48. A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier
    Levent Koç, Thomas A. Mazzuchi, Shahram Sarkani · 2012 · Expert Systems with Applications
49. Handling Missing Values when Applying Classification Models
    Maytal Saar‐Tsechansky, Foster John Provost · 2007 · Faculty Digital Archive (New York University Florence)
50. Learning object identification rules for information integration
    Sheila Tejada, Craig A. Knoblock, Steven Minton · 2001 · Information Systems
51. Forecasting Corn Yield With Machine Learning Ensembles
    Mohsen Shahhosseini, Guiping Hu, Sotirios V. Archontoulis · 2020 · Frontiers in Plant Science
52. Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements
    De‐Cheng Feng, Wenjie Wang, Sujith Mangalathu, Gang Hu, Tao Wu · 2021 · Engineering Structures
53. Human-competitive tagging using automatic keyphrase extraction
    Olena Medelyan, Eibe Frank, Ian H. Witten · 2009 · Conference on Empirical Methods in Natural Language Processing (EMNLP)
54. A review of supervised machine learning algorithms and their applications to ecological data
    Carolina Crisci, Badih Ghattas, Gayan Perera · 2012 · Ecological Modelling
55. Feature selection for ensembles
    David W. Opitz · 1999
56. Data mining with decision trees and decision rules
    Chidanand V. Apte, Sholom M. Weiss · 1997 · Future Generation Computer Systems
57. Stable feature selection for biomarker discovery
    Zengyou He, Weichuan Yu · 2010 · Computational Biology and Chemistry
58. A theoretical and experimental analysis of linear combiners for multiple classifier systems
    Giorgio Fumera, Fabio Roli · 2005 · IEEE Transactions on Pattern Analysis and Machine Intelligence
59. Forecasting stock market crisis events using deep and statistical machine learning techniques
    Sotirios Chatzis, Vassilis Siakoulis, Anastasios S. Petropoulos, Evangelos Stavroulakis, Nikos E. Vlachogiannakis · 2018 · Expert Systems with Applications
60. Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms
    Mahla Nikou, Gholamreza Mansourfar, Jamshid Bagherzadeh · 2019 · Intelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management
61. An improved ensemble learning approach for the prediction of heart disease risk
    Ibomoiye Domor Mienye, Yanxia Sun, Zenghui Wang · 2020 · Informatics in Medicine Unlocked
62. On the class overlap problem in imbalanced data classification
    Pattaramon Vuttipittayamongkol, Eyad Elyan, Andrei Petrovski · 2020 · Knowledge-Based Systems
63. Global and regional analysis of climate and human drivers of wildfire
    Aldersley, Andrew, Murray, Steven J., Cornell, Sarah E. · 2011 · Science of The Total Environment
64. Measurement of branching fractions and rate asymmetries in the rare decays B→K(*)ℓ+ℓ−
    J. P. Lees, Vincent Poireau, V. Tisserand, J. Garra Tico, E. Graugés, ANTIMO PALANO, G. Eigen, B. Stugu, David Norvil Brown, Leroy T. Kerth et al. · 2012 · Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields
65. Phishing Detection System Through Hybrid Machine Learning Based on URL
    Abdul Karim, Mobeen Shahroz, Khabib Mustofa, Samir Brahim Belhouari, S Ramana Kumar Joga · 2023 · IEEE Access
66. A Deep Reinforcement Learning Chatbot
    Iulian Vlad Serban, Chinnadhurai Sankar, Germain, Mathieu, Saizheng Zhang, Zhouhan Lin, Subramanian, Sandeep, Taesup Kim, Pieper, Michael, Sarath Chandar, Nan Rosemary Ke et al. · 2017 · arXiv (Cornell University)
67. Algorithm Selection for Combinatorial Search Problems: A Survey
    Lars Kotthoff · 2016 · Lecture notes in computer science
68. Locally adaptive metric nearest-neighbor classification
    Carlotta Domeniconi, Jing Peng, Dimitrios Gunopulos · 2002 · IEEE Transactions on Pattern Analysis and Machine Intelligence
69. Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project
    Inbal Becker‐Reshef, Chris Justice, Mark Sullivan, Eric F. Vermote, Compton J. Tucker, Assaf Anyamba, J. Small, Ed Pak, E. J. MASUOKA, Jeffrey E. Schmaltz et al. · 2010 · Remote Sensing
70. Adaptive Regression by Mixing
    Yuhong Yang · 2001 · Journal of the American Statistical Association
71. Multiple Classifier System for Remote Sensing Image Classification: A Review
    Peijun Du, Junshi Xia, Zhang We, Kun Tan, Yi Liu, Sicong Liu · 2012 · Sensors
72. Learning from streaming data with concept drift and imbalance: an overview
    Thomas Ryan Hoens, Robi Polikar A, Nitesh V. Chawla · 2012 · Progress in Artificial Intelligence
73. Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information
    Tiantian Yang, Ata Akbari Asanjan, Edwin Welles, Xiaogang Gao, Soroosh Sorooshian, Xiaomang Liu · 2017 · Water Resources Research
74. A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification
    Aytuğ Onan, Serdar Korukoğlu, Hasan Bulut · 2017 · Information Processing & Management
75. Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles
    Wei Chen, Haoyuan Hong, Shaojun Li, Himan Shahabi, Yi Wang, Xiaojing Wang, Baharin Bin Ahmad · 2019 · Journal of Hydrology
76. Neural network ensemble operators for time series forecasting
    Nikolaos Kourentzes, Devon K. Barrow, Sven F. Crone · 2014 · Expert Systems with Applications
77. A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices
    Ana Paula Marques Ramos, Lucas Prado Osco, Danielle Elis Garcia Furuya, Wesley Nunes Gonçalves, Dthenifer Cordeiro Santana, Larissa Pereira Ribeiro Teodoro, Carlos Antônio da Silva, Guilherme Fernando Capristo-Silva, Jonathan Li, Fábio Henrique Rojo Baio et al. · 2020 · Computers and Electronics in Agriculture
78. Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
    Kin G. Olivares, Cristian Challú, Grzegorz Marcjasz, Rafał Weron, Artur W. Dubrawski · 2022 · International Journal of Forecasting
79. A data mining approach for diagnosis of coronary artery disease
    Roohallah Alizadehsani, Jafar Habibi, Mohammad Javad Hosseini, Hoda Mashayekhi, Reihane Boghrati, Asma Ghandeharioun, Behdad Bahadorian, Zahra Alizadeh Sani · 2013 · Computer Methods and Programs in Biomedicine
80. Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
    Esteban Alfaro, Noelia García, Matías Gámez, David A. Elizondo · 2008 · Decision Support Systems
81. Multiple classifiers applied to multisource remote sensing data
    G.J. Briem, Jón Atli Benediktsson, Jóhannes R. Sveinsson · 2002 · IEEE Transactions on Geoscience and Remote Sensing
82. Probabilistic finite-state machines - part I
    Enrique Vidal, Franck Thollard, Colin de la Higuera, Francisco Casacuberta, Rafael C. Carrasco · 2005 · IEEE Transactions on Pattern Analysis and Machine Intelligence
83. Daily streamflow forecasting by machine learning methods with weather and climate inputs
    Kabir Rasouli, William W. Hsieh, Alex J. Cannon · 2011 · Journal of Hydrology
84. Machine learning in geo- and environmental sciences: From small to large scale
    Pejman Tahmasebi, Serveh Kamrava, Tao Bai, Muhammad Sahimi · 2020 · Advances in Water Resources
85. A Survey on Text Classification Algorithms: From Text to Predictions
    Andrea Gasparetto, Matteo Marcuzzo, Alessandro Zangari, Andrea Albarelli · 2022 · Information
86. The evaluation strip: A new and robust method for plotting predicted responses from species distribution models
    Jane Elith, Simon Ferrier, Falk Huettmann, John R. Leathwick · 2005 · Ecological Modelling
87. Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
    Ambra Demontis, Marco Melis, Battista Biggio, Davide Maiorca, Daniel Arp, Konrad Rieck, Igino Corona, Giorgio Giacinto, Fabio Roli · 2017 · IEEE Transactions on Dependable and Secure Computing
88. Predicting Malignant Nodules from Screening CT Scans
    Samuel H. Hawkins, Hua Wang, Ying Liu, Alberto López García, Olya Stringfield, Henry Krewer, Qian Li, Dmitry Cherezov, Robert A. Gatenby, Yoganand Balagurunathan et al. · 2016 · Journal of Thoracic Oncology
89. Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers
    Víctor Manuel Mondéjar-Guerra, Jorge Novo, José Rouco, Manuel G. Penedo, Marcos Ortega · 2018 · Biomedical Signal Processing and Control
90. Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture
    Yemeserach Mekonnen, Srikanth Namuduri, Lamar Burton, Arif I. Sarwat, Shekhar Bhansali · 2019 · Journal of The Electrochemical Society
91. Multitemporal settlement and population mapping from Landsat using Google Earth Engine
    Nirav N. Patel, Emanuele Angiuli, Paolo E. Gamba, Andrea Elizabeth Gaughan, Gianni Lisini, Forrest Robert Stevens, Andrew J. Tatem, Giovanna Trianni · 2014 · International Journal of Applied Earth Observation and Geoinformation
92. Mining data with random forests: current options for real‐world applications
    Andreas Ziegler, Inke Regina König · 2013 · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
93. Decision trees: from efficient prediction to responsible AI
    Hendrik Blockeel, Laurens Devos, Benoît Frénay‬, Géraldin Nanfack, Siegfried Nijssen · 2023 · Frontiers in Artificial Intelligence
94. Error Reduction through Learning Multiple Descriptions
    Kamal Ali, Michael J. Pazzani · 1994 · Machine Learning
95. Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods
    Hui Liu, Chao Chen, Xinwei Lv, Xing Cai Wu, Min Liu · 2019 · Energy Conversion and Management
96. Boosting Neural Networks
    Holger Schwenk, Yoshua Bengio · 2000 · Neural Computation
97. Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region
    Mirela G. Tulbure, Mark Broich, Stephen V. Stehman, Anil R. Kommareddy · 2016 · Remote Sensing of Environment
98. A Comprehensive Investigation of the Role of Imbalanced Learning for Software Defect Prediction
    Qinbao Song, Yuchen Guo, Martin Shepperd · 2018 · IEEE Transactions on Software Engineering
99. Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
    Farzin Kazemi, Neda Asgarkhani, Robert Jankowski · 2023 · Soil Dynamics and Earthquake Engineering
100. An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring
     Loris Nanni, Alessandra Lumini · 2008 · Expert Systems with Applications