Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Using Diversity with Three Variants of Boosting: Aggressive, Conservative, and Inverse
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Boosting and other ensemble methods
Neural Computation
Boosting with averaged weight vectors
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Using Bagging and Cross-Validation to improve ensembles based on penalty terms
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
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As shown in the bibliography, Boosting methods are widely used to build ensembles of neural networks. This kind of methods increases the performance with respect to a single network. Since Freund and Schapire introduced Adaptive Boosting in 1996 some authors have studied and improved Adaboost. In this paper we present Cross Validated Boosting a method based on Adaboost and Cross Validation. We have applied Cross Validation to the learning set in order to get an specific training set and validation set for each network. With this procedure the diversity increases because each network uses an specific validation set to finish its learning. Finally, we have performed a comparison among Adaboost and Crossboost on eight databases from UCI, the results show that Crossboost is the best performing method.