Multilayer Feedforward Ensembles for Classification Problems

Fernández-Redondo, Mercedes; Hernández-Espinosa, Carlos; Torres-Sospedra, Joaquín

doi:10.1007/978-3-540-30499-9_114

Cited by 17 publications

(9 citation statements)

References 6 publications

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“…A simple ensemble can be constructed by training different networks with the same training set, but with different random initialization [2].…”

Section: Traditional Multi-net Modelsmentioning

confidence: 99%

“…This method consists on generating different datasets drawn at random with replacement from the original training set [2].…”

Section: Traditional Multi-net Modelsmentioning

confidence: 99%

“…In Adaptive Boosting, also known as Adaboost, the successive networks are trained with a training set selected randomly from the original set, but the probability of selecting a pattern changes depending on the correct classification of the pattern and on the performance of the last trained network [2].…”

Section: Traditional Multi-net Modelsmentioning

confidence: 99%

“…We have used two different versions of k-fold cross-validation: CVC [2] and CVCv2 [4]. In CVC, the training set is divided into k subsets being k − 1 subsets used to train the network.…”

Section: Traditional Multi-net Modelsmentioning

confidence: 99%

“…Although most of the methods to create a Multi-Net System are based on the Ensemble approach (Boosting, Bagging, Cross-Validation) [2,4] or in the Modular approach (Mixture of Neural Networks) [8,12], we have also analyzed other complex methodologies and models to perform an exhaustive comparison. Stacked Generalization is one of the most known alternatives to the traditional methods previously mentioned.…”

Section: Introductionmentioning

confidence: 99%

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Researching on Multi-net Systems Based on Stacked Generalization

Hernández-Espinosa

Torres-Sospedra

Fernández-Redondo

Artificial Neural Networks in Pattern Recognition

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Abstract. Among the approaches to build a Multi-Net system, Stacked Generalization is 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 Generalization that can be applyied to artificial neural networks. Moreover, two combination methods, Stacked and Stacked+, based on the Stacked Generalization idea were successfully introduced by our research group. In this paper, we want to empirically compare the version of the original Stacked Generalization along 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 Stacked and Stacked+ when they are applied to ensembles previously trained with Simple Ensemble.

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“…A simple ensemble can be constructed by training different networks with the same training set, but with different random initialization [2].…”

Section: Traditional Multi-net Modelsmentioning

confidence: 99%

“…This method consists on generating different datasets drawn at random with replacement from the original training set [2].…”

Section: Traditional Multi-net Modelsmentioning

confidence: 99%

Section: Traditional Multi-net Modelsmentioning

confidence: 99%

“…We have used two different versions of k-fold cross-validation: CVC [2] and CVCv2 [4]. In CVC, the training set is divided into k subsets being k − 1 subsets used to train the network.…”

Section: Traditional Multi-net Modelsmentioning

confidence: 99%