Abstract
The recently introduced concept of Nearest Centroid Neighborhood is applied to discard outliers and prototypes 111 class overlapping regions in order to improve the performance of the Nearest Neighbor rule through an editing procedure, This approach is related to graph based editing algorithms which also define alternative neighborhoods in terms of geornetric relations, Classical editing algorithms are compared to these alternative editing schemes using several synthetic and real data problems. The empirical results show that, the proposed editing algorithm constitutes a good trade-off among performance and computational burden.
Chapter PDF
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
B.B. Chandhuri. A new definition of neighbourhood of a point in multi-dimensional space. Pattern Recognition Letters, 17:11–17, 1996.
T. M. Cover and P. E. Hart. Nearest neighbor pattern classification, IEEE Transactions on Information Theory, 13:21–27, 1967.
B. V. Dasarathy and B. V. Sheela. Visiting nearest neighbors. In Proc. Int. Conf. on Cybernetics and society, pages 630–635, 1977.
P. A. Devijver and J. Kittler. Pattern Recognition. A Statistical Approach. Prentice Hall, 1982.
F. Ferri and E. Vidal. Small sample size effects in the use of editing techniques. In Proc. of 11th International Conference of Pattern Recognition, pages 607–610, The Hague, THE NETHERLANDS, September 1992.
B.K. Bhattacharya G.T. Toussaint and R.S. Poulsen. The application of voronoi diagrams to nonparametric decision rules. In L. Billard, editor, Computer Science and Statistics: The Interface, Elsevier Science, North-Holland, 1985.
L. Kuncheva. Editing for the k-nearest neighbors rule by a genetic algorithm, Pattern Recognition Letters, 16(8):809–814, 1995.
A.E. Lucas and J, Kittler. A comparative study of the kohonen and multiedit neural net learning algorithms, In Proc. 1st IEE Int. Conf.on Artificial Neural Networks, pages 7–11, 1991.
J.E.S. Macleod, A. Luck, and D.M. Titterington. A re-examination of the distance-weighted k-nearest-neighbor classification rule. IEEE Transactions on Systems Man and Cybernetics, 17(4):689–696, 1987.
J.S. Sánchez, F. Pla, and F.J. Ferri. Prototype selection for the nearest neighbour rule through proximity graphs. Pattern Recognition Letters, 18(7):507–513, 1997.
J.S. Sánchez, F. Pla, and F.J. Ferri. On the use of neighbourhood-based non-parametric classifiers. Pattern Recognition Letters, (in press), 1998.
R.D. Short and K. Fukunaga. The optimal distance measure for nearest neighbor classification. IEEE Transactions on Information Theory, 27(5):622–627, 1981.
J. Voisin and P. A. Devijver. An application of the multiedit-condensing technique to the reference selection problem in a print recognition system. Pattern Recognition, 20(5):465–474, 1987.
D. L. Wilson. Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems Man and Cybernetics, 2(3):408–421, 1972.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferri, F.J., Sánchez, J.S., Pla, F. (1998). Editing prototypes in the finite sample size case using alternative neighborhoods. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (eds) Advances in Pattern Recognition. SSPR /SPR 1998. Lecture Notes in Computer Science, vol 1451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033286
Download citation
DOI: https://doi.org/10.1007/BFb0033286
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64858-1
Online ISBN: 978-3-540-68526-5
eBook Packages: Springer Book Archive
