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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Prototype Selection Via Prototype Relevance
verfasst von : J. Arturo Olvera-López, J. Ariel Carrasco-Ochoa, J. Fco. Martínez-Trinidad
Erschienen in: Progress in Pattern Recognition, Image Analysis and Applications
Verlag: Springer Berlin Heidelberg
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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.