Abstract
Finding a minimal subset of objects that correctly classify the training set for the nearest neighbors classifier has been an active research area in Pattern Recognition and Machine Learning communities for decades. Although finding the Minimal Consistent Subset is not feasible in many real applications, several authors have proposed methods to find small consistent subsets. In this paper, we introduce a novel algorithm for this task, based on support graphs. Experiments over a wide range of repository databases show that our algorithm finds consistent subsets with lower cardinality than traditional methods.
Chapter PDF
References
Cover, T., Hart, P.E.: Nearest Neighbor pattern classification. IEEE Trans. on Information Theory 13, 21–27 (1967)
Athitsos, V.: Learning embeddings for indexing, retrieval, and classification, with applications to object and shape recognition in image databases. Vol. Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, p. 156. Boston University (2006)
Wilfong, G.: Nearest neighbor problems. In: 7th Annual ACM Symposium on Computational Geometry, pp. 224–233 (1991)
Hart, P.E.: The condensed nearest neighbor rule. IEEE Trans. on Information Theory 14, 515–516 (1968)
Gates, G.W.: The reduced nearest neighbor rule. IEEE Transactions on Information Theory IT-18, 431–433 (1972)
Dasarathy, B.D.: Minimal consistent set (MCS) identification for optimal nearest neighbor decision systems design. IEEE Transactions on Systems, Man and Cybernetics 24, 511–517 (1994)
Chou, C.-H., Kuo, B.-H., Chang, F.: The Generalized Condensed Nearest Neighbor Rule as a Data Reduction Method. In: 18th International Conference on Pattern Recognition ICPR 2006, Tampa, USA. IEEE, Los Alamitos (2006)
García-Borroto, M., Ruiz-Shulcloper, J.: Selecting Prototypes in Mixed Incomplete Data. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, pp. 450–459. Springer, Heidelberg (2005)
Merz, C.J., Murphy, P.M.: UCI Repository of Machine Learning Databases. University of California at Irvine, Department of Information and Computer Science, Irvine (1998)
Wilson, R.D., Martinez, T.R.: Improved Heterogeneous Distance Functions. Journal of Artificial Intelligence Research 6, 1–34 (1997)
Kuncheva, L.I.: Combining pattern classifiers: methods and algorithms. Wiley-Interscience, Hoboken (2004)
Pudil, P., Novovicova, F.J., Kittler, J.: Floating search methods in feature selection. Pattern Recognit. Lett. 15, 1119–1125 (1993)
Dietterich, T.G.: Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms, vol. 10, pp. 1895–1923. MIT Press, Cambridge (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
García-Borroto, M., Villuendas-Rey, Y., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F. (2009). Finding Small Consistent Subset for the Nearest Neighbor Classifier Based on Support Graphs. In: Bayro-Corrochano, E., Eklundh, JO. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2009. Lecture Notes in Computer Science, vol 5856. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10268-4_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-10268-4_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10267-7
Online ISBN: 978-3-642-10268-4
eBook Packages: Computer ScienceComputer Science (R0)