Bagging predictors

Cited by (1,119)

Items 301–400 of 1,119

1. Machine learning for email spam filtering: review, approaches and open research problems
   Emmanuel Gbenga Dada, Joseph Stephen Bassi, Haruna Chiroma, Shafi’i Muhammad Abdulhamid, Adebayọ Olusọla Adetunmbi, Opeyemi Ajibuwa · 2019 · Heliyon
2. Diversity analysis on imbalanced data sets by using ensemble models
   Shuo Wang, Xin Yao · 2009 · IEEE Symposium on Computational Intelligence and Data Mining (CIDM)
3. Predicting Gene Expression from Sequence
   M Beer, Saeed Tavazoie · 2004 · Cell
4. Mixture of experts: a literature survey
   Saeed Masoudnia, Reza Ebrahimpour · 2012 · Artificial Intelligence Review
5. A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost
   Dahai Zhang, Liyang Qian, Baijin Mao, Can Huang, Bin Huang, Yulin Si · 2018 · IEEE Access
6. Comparing apples and oranges
   Eugene H. Blackstone · 2002 · Journal of Thoracic and Cardiovascular Surgery
7. Machine learning advances for time series forecasting
   Ricardo Masini, Marcelo Cunha Medeiros, Eduardo F. Mendes · 2021 · Journal of Economic Surveys
8. Pattern analysis for machine olfaction: a review
   Ricardo Gutiérrez‐Osuna · 2002 · IEEE Sensors Journal
9. Online Bagging and Boosting
   Nikunj C. Oza · 2006 · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics
10. Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods
    Juan Eugenio Iglesias, Chengyi Liu, Paul M. Thompson, Zhuowen Tu · 2011 · IEEE Transactions on Medical Imaging
11. A Survey on Ensemble Learning for Data Stream Classification
    Gomes, Heitor Murilo, Barddal, Jean Paul, Enembreck, Fabrício, Bifet, Albert · 2018 · ACM Computing Surveys
12. What is Machine Learning? A Primer for the Epidemiologist
    Qifang Bi, Katherine E Goodman, Joshua Kaminsky, Justin Lessler · 2019 · American Journal of Epidemiology
13. Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach
    Yang Bao, Bin Ke, Bin Li, Yingri Julia Yu, Jie Zhang · 2019 · Journal of Accounting Research
14. A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges
    Ansam Khraisat, Ammar Alazab · 2021 · Cybersecurity
15. Phase II Study of Single-Agent Navitoclax (ABT-263) and Biomarker Correlates in Patients with Relapsed Small Cell Lung Cancer
    Charles M. Rudin, Christine L. Hann, Edward B. Garon, Moacyr Ribeiro de Oliveira, Philip Bonomi, D. Ross Camidge, Quincy Siu-Chung Chu, Giuseppe Giaccone, Divis K. Khaira, Suresh S. Ramalingam et al. · 2012 · Clinical Cancer Research
16. From patterns to pathways: gene expression data analysis comes of age
    Donna K. Slonim · 2002 · Nature Genetics
17. A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification
    Devis Tuia, Michele Volpi, Loris Copa, Mikhaïl Kanevski, Jordi Muñoz-Marı́ · 2011 · IEEE Journal of Selected Topics in Signal Processing
18. A Novel Transductive SVM for Semisupervised Classification of Remote-Sensing Images
    Lorenzo Bruzzone, Mingmin Chi, Mattia Marconcini · 2006 · IEEE Transactions on Geoscience and Remote Sensing
19. Data Mining in Healthcare and Biomedicine: A Survey of the Literature
    Illhoi Yoo, Patricia E. Alafaireet, Miroslav D. Marinov, Keila E. Pena-Hernandez, Rajitha Gopidi, Jia-Fu Chang, Lei Hua · 2011 · Journal of Medical Systems
20. Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
    Jingjing Wang, Chunxiao Jiang, Haijun Zhang, Yong Ren, Kwang-Cheng Chen, Lajos Hanzo · 2020 · IEEE Communications Surveys & Tutorials
21. Enhancing the prediction of student performance based on the machine learning XGBoost algorithm
    Amal Asselman, Mohamed Khaldi, Souhaib Aammou · 2021 · Interactive Learning Environments
22. Big data for cyber physical systems in industry 4.0: a survey
    Li Da Xu, Lian Duan · 2018 · Enterprise Information Systems
23. Genome-wide genetic association of complex traits in heterogeneous stock mice
    William S. J. Valdar, Leah C. Solberg, Dominique Gauguier, Stephanie Burnett Heyes, Paul Klenerman, William Cookson, Martin S. Taylor, J. N. P. Rawlins, Richard Mott, Jonathan Flint · 2006 · Nature Genetics
24. Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods
    Jochem Verrelst, Zbyněk Malenovský, Christiaan van der Tol, Gustau Camps‐Valls, Jean‐Philippe Gastellu‐Etchegorry, P. Lewis, Peter R. J. North, J. Moreno · 2018 · Surveys in Geophysics
25. The Effect of Bilateral Internal Thoracic Artery Grafting on Survival During 20 Postoperative Years
    Bruce Whitney Lytle, Eugene H. Blackstone, Joseph F. Sabik, Penny L. Houghtaling, Floyd D. Loop, Delos M. Cosgrove · 2004 · The Annals of Thoracic Surgery
26. Neural Probabilistic Language Models
    Yoshua Bengio, Holger Schwenk, Jean-Sébastien Senécal, Fréderic Morin, Jean‐Luc Gauvain · 2006 · Springer eBooks
27. Is repair preferable to replacement for ischemic mitral regurgitation?
    Alan Marc Gillinov, Per N Wierup, Eugene H. Blackstone, Ehab S. Bishay, Delos M. Cosgrove, Jennifer A. White, Bruce Whitney Lytle, Patrick M. McCarthy · 2001 · Journal of Thoracic and Cardiovascular Surgery
28. Result Analysis of the NIPS 2003 Feature Selection Challenge
    Isabelle Guyon, Steve Gunn, Asa Ben‐Hur, Gideon Dror · 2004
29. Convolutional Neural Network Committees for Handwritten Character Classification
    Dan Claudiu Ciresan, Ueli Meier, Luca Maria Gambardella, Jürgen Schmidhuber · 2011 · IAPR International Conference on Document Analysis and Recognition (ICDAR)
30. Deep Co-Training for Semi-Supervised Image Recognition
    Siyuan Qiao, Wei Shen, Zhishuai Zhang, Bo Wang, Alan Yuille · 2018 · Lecture notes in computer science
31. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series
    Amy Pickens, Matthew C. Hansen, Matthew D. Hancher, Stephen V. Stehman, Alexandra Tyukavina, Peter V. Potapov, Byron Marroquin, Zainab Sherani · 2020 · Remote Sensing of Environment
32. Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States
    David P. Roy, Junchang Ju, Kristi Kline, Pasquale L. Scaramuzza, Valeriy Kovalskyy, Matthew C. Hansen, Thomas R. Loveland, Eric F. Vermote, Chunsun Zhang · 2009 · Remote Sensing of Environment
33. Deep Learning over Multi-field Categorical Data
    Weinan Zhang, Tianming Du, Jun Wang · 2016 · Lecture notes in computer science
34. BagBoosting for tumor classification with gene expression data
    Marcel Dettling · 2004 · Bioinformatics
35. An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping
    Brandon Heung, Hung Chak Ho, Jin Zhang, Anders Jensen Knudby, Chuck Bulmer, Margaret Schmidt · 2015 · Geoderma
36. Machine learning and deep learning—A review for ecologists
    Maximilian Pichler, Florian Hartig · 2023 · Methods in Ecology and Evolution
37. Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series
    Matheus Henrique Dal Molin Ribeiro, Leandro dos Santos Coelho · 2019 · Applied Soft Computing
38. Tree Induction for Probability-Based Ranking
    Provost, Foster, Domingos, Pedro · 2003 · Machine Learning
39. Machine learning methods for crop yield prediction and climate change impact assessment in agriculture
    Andrew Crane‐Droesch · 2018 · Environmental Research Letters
40. Support vector machines for SAR automatic target recognition
    Qun Chao Zhao, José Carlos Príncipe · 2001 · IEEE Transactions on Aerospace and Electronic Systems
41. Improving innovation performance through knowledge acquisition: the moderating role of employee retention and human resource management practices
    Armando Papa, Luca Dezi, Gian Luca Gregori, Jens Mueller, Nicola Miglietta · 2018 · Journal of Knowledge Management
42. Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan
    Jie Dou, Ali P. Yunus, Dieu Tien Bui, Abdelaziz Merghadi, Mehebub Sahana, Zhongfan Zhu, Chi-Wen Chen, Zheng Sheng Han, Binh Thai Pham · 2019 · Landslides
43. Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review
    Ernest Yeboah Boateng, Joseph Otoo, Daniel A. Abaye · 2020 · Journal of Data Analysis and Information Processing
44. Bagging to improve the accuracy of a clustering procedure
    Sandrine Dudoit, Jane Fridlyand · 2003 · Bioinformatics
45. Supervised Learning
    Pádraig Cunningham, Matthieu Cord, Sarah Jane Delany · 2008 · Springer eBooks
46. Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds
    Elena Dumitrescu, Sullivan Hué, Christophe Hurlin Y, Sessi Tokpavi · 2020 · SSRN Electronic Journal
47. Decision forest: Twenty years of research
    Lior Rokach · 2015 · Information Fusion
48. Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy
    Raphael A. Viscarra Rossel, R.N. McGlynn, Alex B. McBratney · 2006 · Geoderma
49. THE FERMI LARGE AREA TELESCOPE ON ORBIT: EVENT CLASSIFICATION, INSTRUMENT RESPONSE FUNCTIONS, AND CALIBRATION
    M. Ackermann, Marco Ajello, Andrea Albert, A. Allafort, William B. Atwood, M. Axelsson, L. Baldini, J. Ballet, Guido Barbiellini, D. Bastieri et al. · 2012 · The Astrophysical Journal Supplement Series
50. Prediction Games and Arcing Algorithms
    Leo Breiman · 1999 · Neural Computation
51. Continuous space language models
    Holger Schwenk · 2006 · Computer Speech & Language
52. What have we learned from a decade of empirical research on growth? Growth Empirics and Reality
    William A. Brock · 2001 · The World Bank Economic Review
53. The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
    Alejandro Pasos Ruiz, Michael Flynn, James Large, Matthew Middlehurst, Anthony J. Bagnall · 2020 · Data Mining and Knowledge Discovery
54. Bagging, Boosting and the Random Subspace Method for Linear Classifiers
    Marina Skurichina, Robert P. W. Duin · 2002 · Pattern Analysis and Applications
55. Gradient boosting machine for modeling the energy consumption of commercial buildings
    Samir Touzani, Jessica Granderson, Samuel Fernandes · 2017 · Energy and Buildings
56. Deep learning models for bankruptcy prediction using textual disclosures
    Feng Mai, Shaonan Tian, Chihoon Lee, Ling Ma · 2018 · European Journal of Operational Research
57. A comparative assessment of ensemble learning for credit scoring
    Gang Wang, Jin‐Xing Hao, Jian Ma, Hongbing Jiang · 2010 · Expert Systems with Applications
58. Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models
    Daniel Asante Otchere, Tarek Omar Arbi Ganat, Raoof Gholami, Syahrir Ridha · 2020 · Journal of Petroleum Science and Engineering
59. Multi-temporal MODIS–Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data
    David P. Roy, Junchang Ju, P. Lewis, Crystal Schaaf, Feng Gao A, Matt Hansen, Erik Lindquist · 2008 · Remote Sensing of Environment
60. Efficient Water Quality Prediction Using Supervised Machine Learning
    Umair Ahmed, Rafia Mumtaz, Hirra Anwar, Asad Ali Shah, Rabia Irfan, José García-Nieto · 2019 · Water
61. Face Recognition Performance: Role of Demographic Information
    Klare, Brendan F., Burge, Mark J., Klontz, Joshua C., Vorder Bruegge, Richard W., Jain, Anil K. · 2012 · IEEE Transactions on Information Forensics and Security
62. A Review of Ensemble Methods in Bioinformatics
    Pengyi Yang, Yee Hwa Yang, Bing Bing Zhou, Albert Y. Zomaya · 2010 · Current Bioinformatics
63. Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter
    Eiji Aramaki, Sachiko Maskawa, Mizuki Morita · 2011
64. The Eras and Trends of Automatic Short Answer Grading
    Steven Burrows, Iryna Gurevych, Benno Stein · 2014 · International Journal of Artificial Intelligence in Education
65. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method
    Bjoern Peters, Alessandro Sette · 2005 · BMC Bioinformatics
66. Ensembles of Deep LSTM Learners for Activity Recognition using Wearables
    Guan, Yu, Plötz, Thomas · 2017 · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
67. Active Learning Methods for Remote Sensing Image Classification
    Devis Tuia, Frédéric Ratle, Fabio Pacifici, Mikhaïl Kanevski, William J. Emery · 2009 · IEEE Transactions on Geoscience and Remote Sensing
68. Transfusion in Coronary Artery Bypass Grafting is Associated with Reduced Long-Term Survival
    Colleen Gorman Koch, Liang Li, Andra Ibrahim Duncan, Tomislav Mihaljevic, Floyd D. Loop, Norman J. Starr, Eugene H. Blackstone · 2006 · The Annals of Thoracic Surgery
69. Machine learning: applications of artificial intelligence to imaging and diagnosis
    James Nichols, Hsien Herbert Chan, Matthew A. B. Baker · 2018 · Biophysical Reviews
70. Statistical and machine learning models in credit scoring: A systematic literature survey
    Xolani Dastile, Turgay Çelik, Moshe Moses Potsane · 2020 · Applied Soft Computing
71. Random Forests and Adaptive Nearest Neighbors
    Lin, Yi, Jeon, Yongho · 2006 · Journal of the American Statistical Association
72. Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review
    Ava Vali, Sara Comai, Matteo Matteucci · 2020 · Remote Sensing
73. Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
    Konstantinos Kamnitsas, Wenjia Bai, Enzo Ferrante, Steven McDonagh, Matthew D. Sinclair, Nick Pawlowski, Martin Rajchl, M. Lee, Bernhard Kainz, Daniel Rueckert et al. · 2018 · Lecture notes in computer science
74. Inferring confidence sets of possibly misspecified gene trees
    Korbinian Strimmer, Andrew Rambaut · 2002 · Proceedings of the Royal Society B Biological Sciences
75. The choice of scaling technique matters for classification performance
    Lucas Amorim, George D. C. Cavalcanti, Rafael M. O. Cruz · 2022 · Applied Soft Computing
76. A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin
    Matthew C. Hansen, David P. Roy, Erik Lindquist, Bernard Adusei, Christopher O. Justice, Alice Altstatt · 2008 · Remote Sensing of Environment
77. OPTIMIZING LONG INTRINSIC DISORDER PREDICTORS WITH PROTEIN EVOLUTIONARY INFORMATION
    KANG PENG, Slobodan Vučetić, Predrag Radivojac A, Celeste J. Brown, A. Keith Dunker, Zoran Obradovic A · 2005 · Journal of Bioinformatics and Computational Biology
78. Machine learning in concrete strength simulations: Multi-nation data analytics
    Jui‐Sheng Chou, Chih‐Fong Tsai, Anh-Duc Pham, Yu-Hsin Lu · 2014 · Construction and Building Materials
79. Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)
    Haoyuan Hong, Junzhi Liu, Dieu Tien Bui, Biswajeet Pradhan, Tri Dev Acharya, Binh Thai Pham, A‐Xing Zhu, Wei Chen, Baharin Bin Ahmad · 2018 · CATENA
80. Random forest meteorological normalisation models for Swiss PM 10 trend analysis
    Stuart K. Grange, David C. Carslaw, Alastair Charles Lewis, Eirini Boleti, Christoph Hueglin · 2018 · Atmospheric chemistry and physics
81. Boosting methods for multi-class imbalanced data classification: an experimental review
    Jafar Tanha, Yousef Abdi, Negin Samadi, Nazila Razzaghi-Asl, Mohammad Asadpour · 2020 · Journal Of Big Data
82. Hierarchical multi-label prediction of gene function
    Zafer Barutçuoğlu, Robert E. Schapire, Olga G. Troyanskaya · 2006 · Bioinformatics
83. MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features
    Peng Jiang, H. Wu, Wenxue Wang, Wei Ma, Xiao Song Sun, Zuhong Lu · 2007 · Nucleic Acids Research
84. A deep learning-based multi-model ensemble method for cancer prediction
    Yawen Xiao, Jun Wu, Zongli Lin, Xiaodong Zhao · 2017 · Computer Methods and Programs in Biomedicine
85. Long-Term Durability of Bioprosthetic Aortic Valves: Implications From 12,569 Implants
    Douglas R. Johnston, Edward G. Soltesz, Nakul Vakil, Jeevanantham Rajeswaran, Eric E. Roselli, Joseph F. Sabik, Nicholas Gerard Smedira, Lars Georg Svensson, Bruce Whitney Lytle, Eugene H. Blackstone · 2015 · The Annals of Thoracic Surgery
86. A category-free neural population supports evolving demands during decision-making
    David Chillón Raposo, Matthew Tyler Kaufman, Anne K. Churchland · 2014 · Nature Neuroscience
87. State-of-the-art review of soft computing applications in underground excavations
    Wengang Zhang, Runhong Zhang, Chongzhi Wu, Anthony T.C. Goh, Suzanne Lacasse, Zhongqiang Liu, Hanlong Liu · 2019 · Geoscience Frontiers
88. Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets
    Robert Bryll, Ricardo Gutiérrez‐Osuna, Francis K. H. Quek · 2003 · Pattern Recognition
89. A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm
    Deliang Sun, Haijia Wen, Danzhou Wang, Jiahui Xu · 2020 · Geomorphology
90. A review of epileptic seizure detection using machine learning classifiers
    Mohammad Khubeb Siddiqui, Rubén Morales-Menéndez, Xiaodi Huang, Nasir Hussain · 2020 · Brain Informatics
91. Understanding transportation modes based on GPS data for web applications
    Yu Zheng, Yukun Chen, Quannan Li, Xing Xie, Wei‐Ying Ma · 2010 · ACM Transactions on the Web
92. The Political Economy of Deforestation in the Tropics*
    Robin Burgess, Matthew C. Hansen, Benjamin Olken, Peter V. Potapov, Stefanie Onder · 2012 · The Quarterly Journal of Economics
93. Artificial intelligence in cancer target identification and drug discovery
    Yujie You, Xin Lai, Yi Feng Pan, Huiru Zheng, Julio Vera, Suran Liu, Senyi Deng, Le Zhang · 2022 · Signal Transduction and Targeted Therapy
94. Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features
    Peijun Du, Alim Samat, Björn Waske, Sicong Liu, Zhenhong Li · 2015 · ISPRS Journal of Photogrammetry and Remote Sensing
95. Constructing support vector machine ensemble
    Hyun‐Chul Kim, Shaoning Pang, Hong-Mo Je, Daijin Kim, Sung Yang Bang · 2003 · Pattern Recognition
96. Clustering using firefly algorithm: Performance study
    J. Senthilnath, S.N. Omkar, V. Mani · 2011 · Swarm and Evolutionary Computation
97. Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain)
    Victor Francisco Rodriguez-Galiano, María Paula Mendes, Maria Jose Garcia‐Soldado, Mario Chica‐Olmo, Luı́s Ribeiro · 2014 · The Science of The Total Environment
98. Screening large-scale association study data: exploiting interactions using random forests
    Kathryn L. Lunetta, Brooke Hayward, Jonathan Segal, Paul Van Eerdewegh · 2004 · BMC Genetics
99. Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data
    Baolin Wu, Tom Abbott, David A. Fishman, Walter J. McMurray, Gil Mor, Kathryn L. Stone, David C. Ward, Kenneth R. Williams, Hongyu Zhao · 2003 · Bioinformatics
100. Impact of Mitral Valve Annuloplasty Combined With Revascularization in Patients With Functional Ischemic Mitral Regurgitation
     Tomislav Mihaljević, Buu-Khanh Lam, Jeevanantham Rajeswaran, Masami Takagaki, Michael S. Lauer, Alan Marc Gillinov, Eugene H. Blackstone, Bruce Whitney Lytle · 2007 · Journal of the American College of Cardiology