Two bagging algorithms with coupled learners to encourage diversity

Authors:
Carlos Valle;Ricardo Ñanculef;Héctor Allende;Claudio Moraga
Affiliations:
Universidad Técnica Federico Santa María, Departamento de Informática, Valparaíso, Chile;Universidad Técnica Federico Santa María, Departamento de Informática, Valparaíso, Chile;Universidad Técnica Federico Santa María, Departamento de Informática, Valparaíso, Chile;European Centre for Soft Computing, Mieres, Asturias, Spain and Dortmund University, Dortmund, Germany
Venue:
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Year:
2007

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Abstract

In this paper, we present two ensemble learning algorithms which make use of boostrapping and out-of-bag estimation in an attempt to inherit the robustness of bagging to overfitting. As against bagging, with these algorithms learners have visibility on the other learners and cooperate to get diversity, a characteristic that has proved to be an issue of major concern to ensemble models. Experiments are provided using two regression problems obtained from UCI.