Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Robust Classification for Imprecise Environments
Machine Learning
Machine Learning
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
Decision Support Systems
Effective spam filtering: A single-class learning and ensemble approach
Decision Support Systems
Encyclopedia of Data Warehousing and Mining, Second Edition
Encyclopedia of Data Warehousing and Mining, Second Edition
Classification algorithm sensitivity to training data with non representative attribute noise
Decision Support Systems
Constructing Ensembles from Data Envelopment Analysis
INFORMS Journal on Computing
Issues in stacked generalization
Journal of Artificial Intelligence Research
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Robust ensemble learning for mining noisy data streams
Decision Support Systems
Ensemble learning for customers targeting
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
A use of DEA-DA to measure importance of R&D expenditure in Japanese information technology industry
Decision Support Systems
Learning ensemble classifiers via restricted Boltzmann machines
Pattern Recognition Letters
DEA analysis of FDI attractiveness for sustainable development: Evidence from Chinese provinces
Decision Support Systems
Applying Ant Colony Optimization to configuring stacking ensembles for data mining
Expert Systems with Applications: An International Journal
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An ensemble of classifiers, or a systematic combination of individual classifiers, often results in better classifications in comparison to a single classifier. However, the question regarding what classifiers should be chosen for a given situation to construct an optimal ensemble has often been debated. In addition, ensembles are often computationally expensive since they require the execution of multiple classifiers for a single classification task. To address these problems, we propose a hybrid approach for selecting and combining data mining models to construct ensembles by integrating Data Envelopment Analysis and stacking. Experimental results show the efficiency and effectiveness of the proposed approach.