Bagging predictors

Cited by (1,119)

Items 801–900 of 1,119

1. A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting
   Karim Sherif Mostafa Hassan Ibrahim, Yuk Feng Huang, Ali Najah Ahmed, Chai Hoon Koo, Ahmed El‐Shafie · 2021 · Alexandria Engineering Journal
2. On the Value of Ensemble Effort Estimation
   Ekrem Kocagüneli, Tim Menzies, Jacky Keung · 2011 · IEEE Transactions on Software Engineering
3. Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy
   Bartosz Krawczyk, Mikel Galar, Łukasz Jeleń, Francisco Herrera · 2015 · Applied Soft Computing
4. A new variable selection approach using Random Forests
   Alexander Hapfelmeier, Kurt Ulm · 2012 · Computational Statistics & Data Analysis
5. Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles
   Handing Wang, Yaochu Jin, Chaoli Sun, John Doherty · 2018 · IEEE Transactions on Evolutionary Computation
6. Cluster and Classification Techniques for the Biosciences
   Alan H. Fielding · 2006 · Cambridge University Press eBooks
7. Ensemble Pruning Via Semi-definite Programming
   Yi Zhang, Samuel Burer, W. Nick Street · 2006
8. A survey of intrusion detection systems based on ensemble and hybrid classifiers
   Abdulla Amin Aburomman, Mamun Bin Ibne Reaz · 2016 · Computers & Security
9. PolyNet: A Pursuit of Structural Diversity in Very Deep Networks
   Xingcheng Zhang, Zhizhong Li, Chen Change Loy, Dahua Lin · 2017 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
10. Artificial Intelligence and Parametric Construction Cost Estimate Modeling: State-of-the-Art Review
    Haytham H. Elmousalami · 2019 · Journal of Construction Engineering and Management
11. Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3
    Nima Pahlevan, Brandon Smith, Krista Alikas, Janet M. Anstee, Cláudio Clemente Faria Barbosa, Caren E. Binding, Mariano Bresciani, Bruno Cremella, Claudia Giardino, Daniela Gurlin et al. · 2022 · Remote Sensing of Environment
12. Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree
    Azam, Zahedi, Islam, Md. Motaharul, Huda, Mohammad Nurul · 2023 · IEEE Access
13. Beating the hold-out: bounds for K-fold and progressive cross-validation
    Blum, Avrim, Kalai, Adam, Langford, John · 1999 · Proceedings of the twelfth annual conference on Computational learning theory
14. An empirical evaluation of bagging and boosting
    Richard Maclin, David W. Opitz · 1997
15. Calibrated Ensemble Forecasts Using Quantile Regression Forests and Ensemble Model Output Statistics
    Maxime Taillardat, Olivier Mestre, Michaël Zamo, Philippe Naveau · 2016 · Monthly Weather Review
16. The Political Economy of Deforestation in the Tropics
    Robin Burgess, Matthew C. Hansen, Benjamin Olken, Peter V. Potapov, Stefanie Sieber · 2011 · National Bureau of Economic Research
17. Business Intelligence for Enterprise Systems: A Survey
    Lian Duan, Li Da Xu · 2012 · IEEE Transactions on Industrial Informatics
18. AccelPrint: Imperfections of Accelerometers Make Smartphones Trackable
    Sanorita Dey, Nirupam Roy, Wenyuan Xu, Romit Roy Choudhury, Srihari Nelakuditi · 2014 · Network and Distributed System Security Symposium
19. Constructing diverse classifier ensembles using artificial training examples
    Prem Melville, Raymond J. Mooney · 2003
20. An ensemble learning framework for anomaly detection in building energy consumption
    Daniel B. Araya, Katarina Grolinger, Hany F. ElYamany, Miriam A. M. Capretz, Girma Bitsuamlak · 2017 · Energy and Buildings
21. Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory
    Tedros M. Berhane, Charles R. Lane, Qiusheng Wu, B. C. Autrey, Oleg Arnoldovich Anenkhonov, Victor V. Chepinoga, Hongxing Liu · 2018 · Remote Sensing
22. High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020
    Alessandro Sorichetta, Graeme Hornby, Forrest Robert Stevens, Andrea Elizabeth Gaughan, Catherine Linard, Andrew J. Tatem · 2015 · Scientific Data
23. An Evaluation of Bagging, Boosting, and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA
    Bardan Ghimire, John S. Rogan, Victor Francisco Rodriguez-Galiano, Prajjwal Kumar Panday, Neeti Neeti · 2012 · GIScience & Remote Sensing
24. META-DES: A dynamic ensemble selection framework using meta-learning
    Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti, Tsang Ing Ren · 2014 · Pattern Recognition
25. Random Projections of Residuals for Digital Image Steganalysis
    Vojtěch Holub, Jessica Fridrich · 2013 · IEEE Transactions on Information Forensics and Security
26. Selecting Best Practices for Effort Estimation
    Tim Menzies, Zhihao Chen, Jairus Hihn, Karen Lum · 2006 · IEEE Transactions on Software Engineering
27. Distributed Training Strategies for the Structured Perceptron
    Ryan McDonald, Keith Hall, Gideon Mann · 2010
28. Computer age statistical inference algorithms, evidence, and data science
    Bradley Efron, Trevor J. Hastie · 2016 · Thammasat University Digital Collections
29. A novel ensemble method for credit scoring: Adaption of different imbalance ratios
    Hongliang He, Wenyu Zhang, Shuai Zhang · 2018 · Expert Systems with Applications
30. Prediction of disordered regions in proteins based on the meta approach
    Takashi Ishida, Kengo Kinoshita · 2008 · Bioinformatics
31. How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark
    Nick F Ryman-Tubb, Paul Krause, Wolfgang Garn · 2018 · Engineering Applications of Artificial Intelligence
32. A comparative study of different machine learning methods on microarray gene expression data
    Mehdi Pirooznia, Jack Y. Yang, Mary Qu Yang, Youping Deng · 2008 · BMC Genomics
33. Optimal weighted nearest neighbour classifiers
    Richard J. Samworth · 2011
34. Using output codes to boost multiclass learning problems
    Robert E. Schapire · 1997
35. Musite, a Tool for Global Prediction of General and Kinase-specific Phosphorylation Sites
    Jianjiong Gao, Jay J. Thelen, A. Keith Dunker, Dong He Xu · 2010 · Molecular & Cellular Proteomics
36. Bagging survival trees
    Torsten Hothorn, Berthold Lausen, Axel Benner, Martin Radespiel‐Tröger · 2003 · Statistics in Medicine
37. Once Upon a Crime
    Andrey Bogomolov, Bruno Lepri, Jacopo Staiano, Nuria Oliver, Fabio Pianesi, Alex Pentland · 2014 · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION
38. Social media sentiment analysis: lexicon versus machine learning
    Chedia Dhaoui, Cynthia M. Webster, Lay Peng Tan · 2017 · Journal of Consumer Marketing
39. Automatically countering imbalance and its empirical relationship to cost
    Nitesh V. Chawla, David A. Cieslak, Lawrence Hall, Ajay Joshi · 2008 · Data Mining and Knowledge Discovery
40. GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment
    Binh Thai Pham, Mohammadtaghi Avand, Saeid Janizadeh, Tran Van Phong, Nadhir Abbas Al-Ansari, Lanh Si Ho, Sumit Das, Hiep Van Le, Ata Amini, Saeid Khosrobeigi Bozchaloei et al. · 2020 · Water
41. Authentic facial expression analysis
    Sebe, N., Lew, M.S., Sun, Y., Cohen, I., Gevers, T., Huang, T.S. · 2007 · Image and Vision Computing
42. Computer-Aided Breast Cancer Detection Using Mammograms: A Review
    Karthikeyan Ganesan, U. Rajendra Acharya, Chua Kuang Chua, Lim Choo Min, K. Thomas Abraham, Kwan-Hoong Ng · 2012 · IEEE Reviews in Biomedical Engineering
43. Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd
    Zichen Wang, Caroline D. Monteiro, Kathleen M. Jagodnik, Nicolas Fernandez, Gregory W. Gundersen, Andrew D. Rouillard, Sherry L. Jenkins, Axel S. Feldmann, Kevin S. Hu, Michael G. McDermott et al. · 2016 · Nature Communications
44. Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography
    Lior Rokach · 2009 · Computational Statistics & Data Analysis
45. Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data
    Ruth DeFries · 2000 · Remote Sensing of Environment
46. Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models
    Baljinder Ghotra, Shane McIntosh, Ahmed E. Hassan · 2015
47. Politicians in the line of fire: Incivility and the treatment of women on social media
    Ludovic Rheault, Erica Rayment, Andreea Musulan · 2019 · Research & Politics
48. Operational Research and Artificial Intelligence Methods in Banking
    Michael Doumpos, Constantin Zopounidis, Dimitrios Gounopoulos, Emmanouil Platanakis, Wenke Zhang · 2022 · European Journal of Operational Research
49. Machine Learning Methods for Property Prediction in Chemoinformatics:Quo Vadis?
    Alexandre A. Varnek, Igor I. Baskin · 2012 · Journal of Chemical Information and Modeling
50. Combining predictions for accurate recommender systems
    Michael Jahrer, Andreas Töscher, Robert A. Legenstein · 2010 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
51. Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing
    Alexandra Melike Brintrup, Johnson Pak, David Ratiney, Tim Pearce, Pascal Wichmann, Philip Woodall, Duncan McFarlane · 2019 · International Journal of Production Research
52. Discernibility and rough sets in medicine : tools and applications.
    Aleksander Øhrn · 2000 · BIBSYS Brage (BIBSYS (Norway))
53. Bayesian Additive Regression Trees
    Hugh Chipman, Edward I. George, Robert E. McCulloch · 2006
54. Probability Machines
    James D. Malley, Jochen Kruppa, Abhijit Dasgupta, Karen G. Malley, Andreas Ziegler · 2011 · Methods of Information in Medicine
55. Meta-learning for time series forecasting and forecast combination
    Christiane Lemke, Bogdan Gabryś · 2010 · Neurocomputing
56. Multi-variate flood damage assessment: a tree-based data-mining approach
    Bruno Merz, Heidi Kreibich, Upmanu Lall · 2013 · Natural hazards and earth system sciences
57. A Discriminative Model for Age Invariant Face Recognition
    Zhifeng Li, Unsang Park, Anil K. Jain · 2011 · IEEE Transactions on Information Forensics and Security
58. A comparative study on base classifiers in ensemble methods for credit scoring
    Joaquín Abellán, Javier G. Castellano · 2016 · Expert Systems with Applications
59. Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification
    Shahab Eddin Jozdani, Brian Alan Johnson, Dongmei Chen · 2019 · Remote Sensing
60. Bagged support vector machines for emotion recognition from speech
    Anjali Bhavan, Pankaj Chauhan, Hitkul, Rajiv Ratn Shah · 2019 · Knowledge-Based Systems
61. Hellinger distance decision trees are robust and skew-insensitive
    David A. Cieslak, Thomas Ryan Hoens, Nitesh V. Chawla, W. Philip Kegelmeyer · 2011 · Data Mining and Knowledge Discovery
62. A machine learning framework for assessing post-earthquake structural safety
    Yu Zhang, Henry V. Burton, Han Sun, Mehrdad Shokrabadi · 2017 · Structural Safety
63. A textual-based featuring approach for depression detection using machine learning classifiers and social media texts
    Raymond Chiong, Gregorius Satia Budhi, Sandeep Dhakal, Fabian Joon Kiong Chiong · 2021 · Computers in Biology and Medicine
64. Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach
    Chen, Xiqun (Michael), Zahiri, Majid, Zhang, Shuaichao · 2017 · Transportation Research Part C: Emerging Technologies
65. Australia-wide predictions of soil properties using decision trees
    Brent L. Henderson, Elisabeth N. Bui, Christopher J. Moran, D. A. P. Simon · 2004 · Geoderma
66. Spatiotemporal exploratory models for broad‐scale survey data
    Daniel Fink, Wesley M. Hochachka, Benjamin Zuckerberg, David W. Winkler, Ben Shaby, M. Arthur Munson, Giles Hooker, Mirek Riedewald, Daniel R. Sheldon, Steve Kelling · 2010 · Ecological Applications
67. HILDA: A Discourse Parser Using Support Vector Machine Classification
    Hugo Hernault, Helmut Prendinger, David A. duVerle, Mitsuru Ishizuka · 2010 · Dialogue & Discourse
68. Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers
    Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease · 2015 · arXiv (Cornell University)
69. Algorithmic Decision-Making Based on Machine Learning from Big Data: Can Transparency Restore Accountability?
    de Laat, Paul B. · 2018 · Philosophy & Technology
70. Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
    Giorgio Valentini, Thomas G. Dietterich · 2004
71. A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: Deterministic forecast of hourly production
    Michaël Zamo, Olivier Mestre, Philippe Arbogast, Olivier Pannekoucke · 2014 · Solar Energy
72. Optimal experimental design and some related control problems
    Luc Pronzato · 2007 · Automatica
73. A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features
    Ahnaf Rashik Hassan, Mohammed Imamul Hassan Bhuiyan · 2016 · Journal of Neuroscience Methods
74. CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services
    Jonathan Burez, Dirk Van den Poel · 2006 · Expert Systems with Applications
75. Stock market prediction with multiple classifiers
    Bo Qian, Khaled Rasheed · 2006 · Applied Intelligence
76. Attribute selection with fuzzy decision reducts
    Chris Cornelis A, Richard Jensen, Germán Alonso Patiño Hurtado, Dominik Ślȩzak · 2009 · Information Sciences
77. CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests
    Li Ma, Suohai Fan · 2017 · BMC Bioinformatics
78. Comprehensive ensemble in QSAR prediction for drug discovery
    Sunyoung Kwon, Ho Bae, Jeonghee Jo, Sungroh Yoon · 2019 · BMC Bioinformatics
79. Two credit scoring models based on dual strategy ensemble trees
    Gang Wang, Jian Ma, Lihua Huang, Kaiqin Xu · 2011 · Knowledge-Based Systems
80. Experimental comparisons of online and batch versions of bagging and boosting
    Nikunj C. Oza, Stuart Russell · 2001 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
81. From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts
    Jehoshua Eliashberg, Sam K. Hui, Z. John Zhang · 2007 · Management Science
82. HECTAR: A method to predict subcellular targeting in heterokonts
    Bernhard Gschloessl, Yann Guermeur, Jeremy Mark Cock · 2008 · BMC Bioinformatics
83. Source localization in an ocean waveguide using supervised machine learning
    Haiqiang Niu, Emma Reeves, Peter Gerstoft · 2017 · The Journal of the Acoustical Society of America
84. GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models
    Wei Chen, Yang Li · 2020 · CATENA
85. Electricity price forecasting on the day-ahead market using machine learning
    Léonard Tschora, Erwan Pierre, Marc Plantevit, Céline Robardet · 2022 · Applied Energy
86. Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
    Javad Hassannataj Joloudari, Abdolreza Marefat, Mohammad Ali Nematollahi, Solomon Sunday Oyelere, Sadiq Hussain · 2023 · Applied Sciences
87. Classifier Fusion With Contextual Reliability Evaluation
    Zhunga Liu, Quan Pan, Jean Dezert, Junwei Han, You He · 2017 · IEEE Transactions on Cybernetics
88. Behind the cues: A benchmarking study for fake news detection
    Georgios Gravanis, Athena I. Vakali, Konstantinos Diamantaras, Panagiotis Karadais · 2019 · Expert Systems with Applications
89. Machine learning prediction of compressive strength for phase change materials integrated cementitious composites
    Afshin Marani, Moncef L. Nehdi · 2020 · Construction and Building Materials
90. An effective approach for software project effort and duration estimation with machine learning algorithms
    Pospieszny, Przemyslaw, Czarnacka-Chrobot, Beata, Kobylinski, Andrzej · 2018 · Journal of Systems and Software
91. Heterogeneous Ensemble-Based Infill Criterion for Evolutionary Multiobjective Optimization of Expensive Problems
    Dan Guo, Yaochu Jin, Jinliang Ding, Tianyou Chai · 2018 · IEEE Transactions on Cybernetics
92. How Intense Are You? Predicting Intensities of Emotions and Sentiments using Stacked Ensemble [Application Notes]
    Akhtar, Md Shad, Ekbal, Asif, Cambria, Erik · 2020 · IEEE Computational Intelligence Magazine
93. Improving Random Forests
    Marko Robnik‐Šikonja · 2004 · Lecture notes in computer science
94. Regularized estimation of large-scale gene association networks using graphical Gaussian models
    Nicole Krämer, Juliane Schäfer, Anne‐Laure Boulesteix · 2009 · BMC Bioinformatics
95. History, Evolution and Future of Big Data and Analytics: A Bibliometric Analysis of Its Relationship to Performance in Organizations
    Saša Batistič, Paul A. van der Laken · 2019 · British Journal of Management
96. Gaining Insight Into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools
    Murat Kuzlu, Ümit Cali, Vinayak Sharma, Özgur Güler · 2020 · IEEE Access
97. Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam
    Binh Thai Pham, Chinh Luu, Tran Van Phong, Huu Duy Nguyen, Hiep Van Le, Thai Quoc Tran, Huong Thu Ta, Indra Prakash · 2020 · Journal of Hydrology
98. Criteria for the translation of radiomics into clinically useful tests
    Erich P. Huang, James P.B. O’Connor, Lisa M. McShane, Maryellen Lissak Giger, Philippe Lambin, Paul E. Kinahan, Eliot L. Siegel, Lalitha Krishna Shankar · 2022 · Nature Reviews Clinical Oncology
99. Predicting types of occupational accidents at construction sites in Korea using random forest model
    Kyungsu Kang, Han-Guk Ryu · 2019 · Safety Science
100. Evolutionary Machine Learning: A Survey
     Akbar Telikani, Amirhessam Tahmassebi, Wolfgang Banzhaf, Amir H. Gandomi · 2021 · ACM Computing Surveys