Abstract
The Nearest Neighbor classifier is a simple but powerful non-parametric technique for supervised classification. However, it is very sensitive to noise and outliers, which could decrease the classifier accuracy. To overcome this problem, we propose two new editing methods based on maximum similarity graphs. Numerical experiments in several databases show the high quality performance of our methods according to classifier accuracy.
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García-Borroto, M., Villuendas-Rey, Y., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F. (2009). Using Maximum Similarity Graphs to Edit Nearest Neighbor Classifiers. In: Bayro-Corrochano, E., Eklundh, JO. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2009. Lecture Notes in Computer Science, vol 5856. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10268-4_57
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DOI: https://doi.org/10.1007/978-3-642-10268-4_57
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