Abstract
It is a widely accepted fact that no single Machine Learning System (MLS) gets the smaller classification error on all data sets. Different algorithms fit better to certain problems and it is interesting to combine them in some way to improve the overall accuracy. In this paper, we propose a method to construct a new MLS from given ones. It is based on the selection of the system that will perform better on a particular data set. We study several ways of selecting the systems and carry out experiments with well-known MLS on the Holte data set.
The research reported in this paper has been supported in part under MCyT and Feder grant TIC2001-3579
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Quevedo, J.R., Combarro, E.F., Bahamonde, A. (2003). Choosing among algorithms to improve accuracy. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_32
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DOI: https://doi.org/10.1007/3-540-44868-3_32
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