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- W1569149617 abstract "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." @default.
- W1569149617 created "2016-06-24" @default.
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- W1569149617 date "2008-08-12" @default.
- W1569149617 modified "2023-09-26" @default.
- W1569149617 title "Researching on Multi-net Systems Based on Stacked Generalization" @default.
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- W1569149617 doi "https://doi.org/10.1007/978-3-540-69939-2_19" @default.
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