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Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, 2010, Volume 100, Issue 1, pp. 1 - 11
Journal Article
Expert Systems With Applications, ISSN 0957-4174, 12/2015, Volume 42, Issue 22, pp. 8484 - 8496
Journal Article
IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, 09/2018, Volume 29, Issue 9, pp. 4504 - 4509
Journal Article
Expert Systems With Applications, ISSN 0957-4174, 10/2017, Volume 83, pp. 405 - 417
Journal Article
Knowledge-Based Systems, ISSN 0950-7051, 02/2015, Volume 75, pp. 141 - 151
The idea of ensemble methodology is to combine multiple predictive models in order to achieve a better prediction performance. In this task we analyze the... 
Ant Colony Optimization | Boosting | Meta-ensemble | Bagging | Random Forest | Ant Colony Decision Forest | TREES | OPTIMIZATION | ALGORITHMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Accuracy | Tasks | Methodology | Data sets | Classification | Mathematical models | Decision trees
Journal Article
Journal of Hydrology, ISSN 0022-1694, 01/2013, Volume 477, pp. 119 - 128
► We use classification and regression trees for streamflow forecasting, first. ► We employ the support vector regression model as the benchmark model. ► We... 
Streamflow prediction | Support vector regression | Ensemble learning | Classification and regression trees | Bagging (bootstrap aggregating) | Stochastic gradient boosting | ENGINEERING, CIVIL | GEOSCIENCES, MULTIDISCIPLINARY | WATER RESOURCES | NEURAL-NETWORK ENSEMBLES | MACHINE | Streamflow | Analysis
Journal Article
Decision Support Systems, ISSN 0167-9236, 01/2014, Volume 57, Issue 1, pp. 77 - 93
With the rapid development of information technologies, user-generated contents can be conveniently posted online. While individuals, businesses, and... 
Boosting | Ensemble learning | Bagging | Random Subspace | Sentiment classification | FEATURE-SELECTION | WEB FORUMS | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | COMPUTER SCIENCE, INFORMATION SYSTEMS | MODEL | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Assessments
Journal Article
Expert Systems With Applications, ISSN 0957-4174, 03/2009, Volume 36, Issue 2, pp. 2161 - 2176
This paper provides insights on advantages and disadvantages of two ensemble models: ensembles based on sampling and feature selection. Experimental results... 
Feature selection | Database marketing | Ensembles | Bagging | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | AREA | MODELS | ROC CURVE | ALGORITHMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | OPERATING CHARACTERISTIC CURVES | ENGINEERING, ELECTRICAL & ELECTRONIC | Databases | Marketing
Journal Article
Journal of Statistical Software, ISSN 1548-7660, 2013, Volume 54, Issue 2, pp. 1 - 35
Boosting and bagging are two widely used ensemble methods for classification. Their common goal is to improve the accuracy of a classifier combining single... 
AdaBoost.M1 | Classification trees | R program | Bagging | Classification | SAMME | ENSEMBLE | ADABOOST | bagging | STATISTICS & PROBABILITY | ALGORITHMS | classification | CLASSIFIERS | PREDICTION | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | MODELS | classification trees
Journal Article
Science of the Total Environment, ISSN 0048-9697, 06/2019, Volume 668, pp. 1038 - 1054
Journal Article
Ecology, ISSN 0012-9658, 1/2007, Volume 88, Issue 1, pp. 243 - 251
Journal Article
Expert Systems With Applications, ISSN 0957-4174, 01/2011, Volume 38, Issue 1, pp. 223 - 230
Both statistical techniques and Artificial Intelligence (AI) techniques have been explored for credit scoring, an important finance activity. Although there... 
Boosting | Ensemble learning | Stacking | Credit scoring | Bagging | SUPPORT VECTOR MACHINES | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | NEURAL-NETWORKS | MODELS | RISK | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Business schools | Comparative analysis | Neural networks | Artificial intelligence | Credit ratings
Journal Article
Information Sciences, ISSN 0020-0255, 08/2016, Volume 354, pp. 178 - 196
•The use of ordering based pruning approaches for ensemble learning in imbalanced classification is proposed.•Standard pruning schemes have been adapted to the... 
Boosting | Tree-based ensembles | Imbalanced datasets | Ordering-based pruning | Bagging | DATA-SETS | COMPUTER SCIENCE, INFORMATION SYSTEMS | CLASSIFICATION | ALGORITHMS | MAPREDUCE | INSIGHT | ACCURACY | TREES | BIG DATA | DIVERSITY | SELECTION | Computer science | Analysis | Learning | Classifiers | State of the art | Classification | Pruning | Mathematical models | Complement | Gain
Journal Article