Abstract
In Pattern recognition, the supervised classifiers use a training set T for classifying new prototypes. In practice, not all information in T is useful for classification therefore it is necessary to discard irrelevant prototypes from T. This process is known as prototype selection, which is an important task for classifiers since through this process the time in the training and/or classification stages could be reduced. Several prototype selection methods have been proposed following the Nearest Neighbor (NN) rule; in this work, we propose a new prototype selection method based on the prototype relevance and border prototypes, which is faster (over large datasets) than the other tested prototype selection methods. We report experimental results showing the effectiveness of our method and compare accuracy and runtimes against other prototype selection methods.
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Olvera-López, J.A., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F. (2008). Prototype Selection Via Prototype Relevance. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2008. Lecture Notes in Computer Science, vol 5197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85920-8_19
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DOI: https://doi.org/10.1007/978-3-540-85920-8_19
Publisher Name: Springer, Berlin, Heidelberg
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