On a unified framework for sampling with and without replacement in decision tree ensembles

Authors:
J. M. Martínez-Otzeta;B. Sierra;E. Lazkano;E. Jauregi
Affiliations:
Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia-San Sebastián, Basque Country, Spain;Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia-San Sebastián, Basque Country, Spain;Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia-San Sebastián, Basque Country, Spain;Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia-San Sebastián, Basque Country, Spain
Venue:
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
Year:
2006

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Abstract

Classifier ensembles is an active area of research within the machine learning community. One of the most successful techniques is bagging, where an algorithm (typically a decision tree inducer) is applied over several different training sets, obtained applying sampling with replacement to the original database. In this paper we define a framework where sampling with and without replacement can be viewed as the extreme cases of a more general process, and analyze the performance of the extension of bagging to such framework.