Abstract
Two algorithms for reference set size reduction are presented and tested on an actual data set of a large size. The first one, as most of existing procedures, is based on the consistency idea, which means that all points from the primary reference set are correctly classified by 1-NN rule operating with the reduced set. The second algorithm requires division of the reference set into some subsets and replacing these subsets by their gravity centers. These gravity centers assume the same label as the majority of points of the corresponding subset. This algorithm enables the condensation of the reference set to the desired size, however, the resulting sets do not offer as good classification quality as other existing methods.
As opposed to the first algorithm, the second algorithm does not enable the control of the reduced set size. It is shown that combining both of these algorithms promises as good of a performance while allowing the control of the size of the obtained condensed sets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Fix E, Hodges JL (1952) Discriminatory Analysis: Nonparametric Discrimination Small Sample Performance, Project 21-49-004, Report Number 11, USAF School of Aviation Medicine, Randolph Field, Texas: 280–322, reprinted in the book: Dasarathy BV (1991) NN Pattern Classification Techniques, IEEE Computer Society Press: 40–56.
Hart PE (1968) The condensed nearest neighbor rule, IEEE Trans. Information Theory, Vol. 14, No. 3: 515–516.
Gowda KC, Krishna G (1979) The condensed nearest neighbor rule using the concept of mutual nearest neighborhood, IEEE Trans. Information Theory, Vol. 25, No. 4: 488–490.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jóźwik, A., Kieś, P. (2005). Reference Set Size Reduction for 1-NN Rule Based on Finding Mutually Nearest and Mutually Furthest Pairs of Points. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_21
Download citation
DOI: https://doi.org/10.1007/3-540-32390-2_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25054-8
Online ISBN: 978-3-540-32390-7
eBook Packages: EngineeringEngineering (R0)