Abstract
Ensemble methods improve accuracy by combining the predictions of a set of different hypotheses. A well-known method for generating hypothesis ensembles is Bagging. One of the main drawbacks of ensemble methods in general, and Bagging in particular, is the huge amount of computational resources required to learn, store, and apply the set of models. Another problem is that even using the bootstrap technique, many simple models are similar, so limiting the ensemble diversity. In this work, we investigate an optimization technique based on sharing the common parts of the models from an ensemble formed by decision trees in order to minimize both problems. Concretely, we employ a structure called decision multi-tree which can contain simultaneously a set of decision trees and hence consider just once the ”repeated” parts. A thorough experimental evaluation is included to show that the proposed optimisation technique pays off in practice.
This work has been partially supported by CICYT under grant TIC2001-2705-C03-01 and MCyT Acción Integrada HU 2003-0003.
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Estruch, V., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J. (2004). Bagging Decision Multi-trees. In: Roli, F., Kittler, J., Windeatt, T. (eds) Multiple Classifier Systems. MCS 2004. Lecture Notes in Computer Science, vol 3077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25966-4_4
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DOI: https://doi.org/10.1007/978-3-540-25966-4_4
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