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.
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Martínez-Otzeta, J.M., Sierra, B., Lazkano, E., Jauregi, E. (2006). On a Unified Framework for Sampling With and Without Replacement in Decision Tree Ensembles. In: Euzenat, J., Domingue, J. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2006. Lecture Notes in Computer Science(), vol 4183. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861461_14
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DOI: https://doi.org/10.1007/11861461_14
Publisher Name: Springer, Berlin, Heidelberg
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