Abstract
As seen in the bibliography, Adaptive Boosting (Adaboost) is one of the most known methods to increase the performance of an ensemble of neural networks. We introduce a new method based on Adaboost where we have applied Cross-Validation to increase the diversity of the ensemble. We have used Cross-Validation over the whole learning set to generate an specific training set and validation set for each network of the committee. We have tested Adaboost and Crossboost with seven databases from the UCI repository. We have used the mean percentage of error reduction and the mean increase of performance to compare both methods, the results show that Crossboost performs better.
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Torres-Sospedra, J., Hernández-Espinosa, C., Fernández-Redondo, M. (2006). Improving Adaptive Boosting with k-Cross-Fold Validation. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_46
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DOI: https://doi.org/10.1007/11816157_46
Publisher Name: Springer, Berlin, Heidelberg
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