Abstract
In supervised classification, the object selection or instance selection is an important task, mainly for instance-based classifiers since through this process the time in training and classification stages could be reduced. In this work, we propose a new mixed data object selection method based on clustering and border objects. We carried out an experimental comparison between our method and other object selection methods using some mixed data classifiers.
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Olvera-López, J.A., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A. (2007). Mixed Data Object Selection Based on Clustering and Border Objects. In: Rueda, L., Mery, D., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2007. Lecture Notes in Computer Science, vol 4756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76725-1_70
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DOI: https://doi.org/10.1007/978-3-540-76725-1_70
Publisher Name: Springer, Berlin, Heidelberg
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