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Title: Dynamic k-NN Classification Based on Region Homogeneity
Authors: Ougiaroglou, Stefanos
Evangelidis, Georgios
Diamantaras, Konstantinos I.
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: k-NN classification
Dynamic k parameter determination
Homogeneous clustering
Issue Date: 17-Aug-2020
Volume: 1259
First Page: 27
Last Page: 37
Volume Title: New Trends in Databases and Information Systems
Part of Series: Communications in Computer and Information Science
Part of Series: Communications in Computer and Information Science
Abstract: The effectiveness of the k-NN classifier is highly dependent on the value of the parameter k that is chosen in advance and is fixed during classification. Different values are appropriate for different datasets and parameter tuning is usually inevitable. A dataset may include simultaneously well-separated and not well-separated classes as well as noise in certain regions of the metric space. Thus, a different k value should be employed depending on the region where the unclassified instance lies. The paper proposes a new algorithm with five heuristics for dynamic k determination. The heuristics are based on a fast clustering pre-processing procedure that builds an auxiliary data structure. The latter provides information about the region where the unclassified instance lies. The heuristics exploit the information and dynamically determine how many neighbours will be examined. The data structure construction and the heuristics do not involve any input parameters. The proposed heuristics are tested on several datasets. The experimental results illustrate that in many cases they can achieve higher classification accuracy than the k-NN classifier that uses the best tuned k value.
ISBN: 978-3-030-54622-9
ISSN: 1865-0929
Other Identifiers: 10.1007/978-3-030-54623-6_3
Appears in Collections:Department of Applied Informatics

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