Abstract
Edition is an important and useful task in supervised classification specifically for instance-based classifiers because edition discards from the training set those useless or harmful objects for the classification accuracy and it helps to reduce the size of the original training sample and to increase both the classification speed and accuracy. In this paper, we propose two edition schemes that combine edition methods and sequential search for instance selection. In addition, we present an empirical comparison between these schemes and some other edition methods.
This work was financially supported by CONACyT (México) through the project J38707-A.
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Olvera-López, J.A., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A. (2005). Edition Schemes Based on BSE. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_38
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DOI: https://doi.org/10.1007/11578079_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
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