The k-Nearest Neighbor (k-NN) classifier is a widely-used and effective classification method. The main k-NN drawback is that it involves high computational cost when applied on large datasets. Many Data Reduction Techniques have been proposed in order to speed-up the classification process. However, their effectiveness depends on the level of noise in the data. This paper shows that the k-means clustering algorithm can be used as a noise-tolerant Data Reduction Technique. The conducted experimental study illustrates that if the reduced dataset includes the k-means centroids as representatives of the initial data, performance is not negatively affected as much by the addition of noise. © 2012 Springer-Verlag.
CITATION STYLE
Ougiaroglou, S., & Evangelidis, G. (2012). A simple noise-tolerant abstraction algorithm for fast k-NN classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7209 LNAI, pp. 210–221). https://doi.org/10.1007/978-3-642-28931-6_20
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