Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Stacked generalization: when does it work?
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Issues in stacked generalization
Journal of Artificial Intelligence Research
Combining MF networks: a comparison among statistical methods and stacked generalization
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
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Among the approaches to build a Multi-Net system, Stacked Generalizationis a well-known model. The classification system is divided into two steps. Firstly, the level-0 generalizers are built using the original input data and the class label. Secondly, the level-1 generalizers networks are built using the outputs of the level-0 generalizers and the class label. Then, the model is ready for pattern recognition. We have found two important adaptations of Stacked Generalizationthat can be applyied to artificial neural networks. Moreover, two combination methods, Stackedand Stacked+, based on the Stacked Generalizationidea were successfully introduced by our research group. In this paper, we want to empirically compare the version of the original Stacked Generalizationalong with other traditional methodologies to build Multi-Net systems. Moreover, we have also compared the combiners we proposed. The best results are provided by the combiners Stackedand Stacked+ when they are applied to ensembles previously trained with Simple Ensemble.