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Title: Dealing with noisy data in the context of k-NN Classification
Authors: Ougiaroglou, Stefanos
Evangelidis, Georgios
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: k-NN classification
noisy data
Issue Date: Sep-2015
Volume: 28
First Page: 1
Last Page: 4
Volume Title: BCI'15: Proceedings of the 7th Balkan Conference on Informatics Conference
Abstract: Like many other classifiers, k-NN classifier is noise-sensitive. Its accuracy highly depends on the quality of the training data. Noise and mislabeled data, as well as outliers and overlaps between data regions of different classes, lead to less accurate classification. This problem can be dealt with by adopting either a large k value or by pre-processing the training set with an editing algorithm. The first strategy involves trial-and-error attempts to tune the value of k, while the second strategy constitutes a time-consuming pre-processing step. This paper discusses and compares these two strategies and reveals their advantages and drawbacks.
ISBN: 9781450333351
Other Identifiers: 10.1145/2801081.2801116
Appears in Collections:Department of Applied Informatics

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