Abstract
In this paper, a new approach to training set size reduction is presented. This scheme basically consists of defining a small number of prototypes that represent all the original instances. Although the ultimate aim of the algorithm proposed here is to obtain a strongly reduced training set, the performance is empirically evaluated over nine real datasets by comparing not only the reduction rate but also the classification accuracy with those of other condensing techniques.
This work has been supported by grants TIC2000-1703-C03-03 and CPI2001-2956- C02-02 from CICYT Ministerio de Ciencia y Tecnología and project IST-2001-37306 from European Union.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)
Ainslie, M.C., Sánchez, J.S.: Space partitioning for instance reduction in lazy learning algorithms. In: 2nd Workshop on Integration and Collaboration Aspects of Data Mining, Decision Suport and Meta-Learning, pp. 13–18 (2002)
Bezdek, J.C., Kuncheva, L.I.: Nearest prototype classifier designs: an experimental study. International Journal of Intelligent Systems 16, 1445–1473 (2001)
Chang, C.L.: Finding prototypes for nearest neighbor clasifiers. IEEE Trans. on Computers 23, 1179–1184 (1974)
Chaudhuri, B.B.: A new definition of neighbourhood of a point in multidimensional space. Pattern Recognition Letters 17, 11–17 (1996)
Chen, C.H., Józwik, A.: A sample set condensation algorithm for the class sensitive artificial neural network. Pattern Recognition Letters 17, 819–823 (1996)
Dasarathy, B.V.: Nearest neighbor (NN) norms: NN pattern classification techniques. IEEE Computer Society Press, Los Alamitos (1990)
Dasarathy, B.V.: Minimal consistent subset (mcs) identification for optimal nearest neighbor decision systems design. IEEE Trans. on Systems, Man, and Cybernetics 24, 511–517 (1994)
Devijver, P.A., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice Hall, Englewood Cliffs (1982)
Hart, P.: The condensed nearest neigbor rule. IEEE Trans on Information Theory 14, 505–516 (1968)
Merz, C.J., Murphy, P.M.: UCI Repository of Machine Learning Databases. Dept. of Information and Computer Science. U. of California, Irvine (1998)
Sánchez, J.S., Pla, F., Ferri, F.J.: On the use of neighbourhood-based nonparametric classifiers. Pattern Recognition Letters 18, 1179–1186 (1997)
Wilson, D.L.: Asymptotic properties of nearest neigbor rules using edited data sets. IEEE Trans. on Systems, Man and Cybernetics 2, 408–421 (1972)
Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Machine Learning 38, 257–286 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lozano, M., Sánchez, J.S., Pla, F. (2003). Reducing Training Sets by NCN-based Exploratory Procedures. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_53
Download citation
DOI: https://doi.org/10.1007/978-3-540-44871-6_53
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40217-6
Online ISBN: 978-3-540-44871-6
eBook Packages: Springer Book Archive