Using Maximum Similarity Graphs to Edit Nearest Neighbor Classifiers
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.
Keywordsnearest neighbor error-based editing prototype selection
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