Abstract
The Nearest Neighbor classifier is one of the most popular supervised classification methods. It is very simple, intuitive and accurate in a great variety of real-world applications. Despite its simplicity and effectiveness, practical use of this rule has been historically limited due to its high storage requirements and the computational costs involved, as well as the presence of outliers. In order to overcome these drawbacks, it is possible to employ a suitable prototype selection scheme, as a way of storage and computing time reduction and it usually provides some increase in classification accuracy. Nevertheless, in some practical cases prototype selection may even produce a degradation of the classifier effectiveness. From an empirical point of view, it is still difficult to know a priori when this method will provide an appropriate behavior. The present paper tries to predict how appropriate a prototype selection algorithm will result when applied to a particular problem, by characterizing data with a set of complexity measures.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chang, C.-L.: Finding prototypes for nearest neighbor classifiers. IEEE Trans. on Computers 23, 1179–1184 (1974)
Chavez, E., Navarro, G., Baeza-Yates, R.A., Marroquin, J.L.: Searching in metric spaces. ACM Computing Surveys 33, 273–321 (2001)
Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. on Information Theory 13, 21–27 (1967)
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.E.: The condensed nearest neighbor rule. IEEE Trans. on Information Theory 14, 515–516 (1968)
Ho, T.-K., Basu, M.: Complexity measures of supervised classification problems. IEEE Trans. on Pattern Analysis and Machine Intelligence 24, 289–300 (2002)
Bernardo, E., Ho, T.-K.: On classifier domain of competence. In: Proc. 17th. Int. Conf. on Pattern Recognition 1, Cambridge, UK, pp. 136–139 (2004)
Kim, S.-W., Oommen, B.J.: Enhancing prototype reduction schemes with LVQ3-type algorithms. Pattern Recognition 36, 1083–1093 (2003)
Kuncheva, L.I.: Editing for the k-nearest neighbors rule by a genetic algorithm. Pattern Recognition Letters 16, 809–814 (1995)
Mollineda, R.A., Ferri, F.J., Vidal, E.: An efficient prototype merging strategy for the condensed 1-NN rule through class-conditional hierarchical clustering. Pattern Recognition 35, 2771–2782 (2002)
Ritter, G.L., Woodruff, H.B., Lowry, S.R., Isenhour, T.L.: An algorithm for a selective nearest neighbour decision rule. IEEE Trans. on Information Theory 21, 665–669 (1975)
Tomek, I.: An experiment with the edited nearest neighbor rule. IEEE Trans. on Systems, Man and Cybernetics 6, 448–452 (1976)
Wilson, D.L.: Asymptotic properties of nearest neighbor 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
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mollineda, R.A., Sánchez, J.S., Sotoca, J.M. (2005). Data Characterization for Effective Prototype Selection. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_4
Download citation
DOI: https://doi.org/10.1007/11492542_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26154-4
Online ISBN: 978-3-540-32238-2
eBook Packages: Computer ScienceComputer Science (R0)