Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
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Machine Learning
Machine Learning
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
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Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
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EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Inference for the Generalization Error
Machine Learning
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
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Journal of Artificial Intelligence Research
Switching class labels to generate classification ensembles
Pattern Recognition
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
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IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
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Neurocomputing
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Knowledge-Based Systems
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Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
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This article investigates the properties of class-switching ensembles composed of neural networks and compares them to class-switching ensembles of decision trees and to standard ensemble learning methods, such as bagging and boosting. In a class-switching ensemble, each learner is constructed using a modified version of the training data. This modification consists in switching the class labels of a fraction of training examples that are selected at random from the original training set. Experiments on 20 benchmark classification problems, including real-world and synthetic data, show that class-switching ensembles composed of neural networks can obtain significant improvements in the generalization accuracy over single neural networks and bagging and boosting ensembles. Furthermore, it is possible to build medium-sized ensembles (~200 networks) whose classification performance is comparable to larger class-switching ensembles (~1000 learners) of unpruned decision trees.