Improving boosting methods by generating specific training and validation sets

Authors:
Joaquín Torres-Sospedra;Carlos Hernández-Espinosa;Mercedes Fernández-Redondo
Affiliations:
Department of Computer Science and Engineering, Universitat Jaume I, Castellón, Spain;Department of Computer Science and Engineering, Universitat Jaume I, Castellón, Spain;Department of Computer Science and Engineering, Universitat Jaume I, Castellón, Spain
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

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Abstract

In previous researches it can been seen that Bagging, Boosting and Cross-Validation Committee can provide good performance separately. In this paper, Boosting methods are mixed with Bagging and Cross-Validation Committee in order to generate accurate ensembles and take benefit from all these alternatives. In this way, the networks are trained according to the boosting methods but the specific training and validation set are generated according to Bagging or Cross-Validation. The results show that the proposed methodologies BagBoosting and Cross-Validated Boosting outperform the original Boosting ensembles.