Using Maximum Similarity Graphs to Edit Nearest Neighbor Classifiers

Authors:
Milton García-Borroto;Yenny Villuendas-Rey;Jesús Ariel Carrasco-Ochoa;José Fco. Martínez-Trinidad
Affiliations:
Bioplantas Center, UNICA, C. de Ávila, Cuba and National Institute of Astrophysics,Optics and Electronics, Puebla, México;Ciego de Ávila University UNICA, C. de Ávila, Cuba;National Institute of Astrophysics,Optics and Electronics, Puebla, México;National Institute of Astrophysics,Optics and Electronics, Puebla, México
Venue:
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2009

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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.