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Title: A Simple Noise-Tolerant Abstraction Algorithm for Fast k-NN Classification
Authors: Ougiaroglou, Stefanos
Evangelidis, Georgios
Editors: Corchado, E.
Snášel, V.
Abraham, A.
Woźniak, M.
Graña, M.
Cho, SB.
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: k-NN classification
noisy data
data reduction
Issue Date: 2012
Volume: 7209
First Page: 210
Last Page: 221
Volume Title: Hybrid Artificial Intelligent Systems
Part of Series: Lecture Notes in Computer Science
Part of Series: Lecture Notes in Computer Science
Abstract: 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.
ISBN: 978-3-642-28930-9
ISSN: 0302-9743
Other Identifiers: 10.1007/978-3-642-28931-6_20
Appears in Collections:Department of Applied Informatics

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