Abstract
In this paper we introduce a new method for selecting prototypes with Mixed Incomplete Data (MID) object description, based on an extension of the Nearest Neighbor rule. This new rule allows dealing with functions that are not necessarily dual functions of distances. The introduced compact set editing method (CSE) constructs a prototype consistent subset, which is also subclass consistent. The experimental results show that CSE has a very nice computational behavior and effectiveness, reducing around 50% of prototypes without appreciable degradation on accuracy, in almost all databases with more than 300 objects.
Keywords
- Object Description
- Similar Neighbor
- Neighbor Rule
- Prototype Selection
- Proximity Graph
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.
Chapter PDF
References
Martínez-Trinidad, F., Guzmán-Arenas, A.: The logical combinatorial approach to Pattern Recognition, an overview through selected works. Pattern Recognition 34, 741–751 (2001)
Ruiz-Shulcloper, J., Abidi, M.A.: Logical combinatorial pattern recognition: A Review. In: Pandalai, S.G. (ed.) Recent Research Developments in Pattern Recognition, Transword Research Networks, USA
Merz, C.J., Murphy, P.M.: UCI Repository of Machine Learning Databases. Technical report, University of California at Irvine, Department of Information and Computer Science (1998)
Sato, M., Sato, Y.: Extended fuzzy clustering models for asymmetric similarity. In: Bouchon-Meunier, B., Yager, R., Zadeh, L. (eds.) Fuzzy logic and soft computing. World Scientific, Singapore
Chen, H., Lynch, K.J.: Automatic construction of networks of concepts characterizing document databases. IEEE Transactions on systems, man and cybernetics 22, 885–902 (1992)
Hart, P.E.: The condensed nearest neighbor rule. IEEE Trans. on Information Theory 14, 515–516 (1968)
Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on systems, man and cybernetics SMC-2, 408–421 (1972)
Kuncheva, L.I., Bezdek, J.C.: Nearest prototype classification: clustering, genetic algorithms or random search. IEEE transactions on systems, man and cybernetics. Part C 28, 160–164 (1998)
Kim, S.-W., Oommen, J.B.: A brief taxonomy and ranking of creative prototype reduction schemes. In: IEEE SCM Conference (2002)
Toussaint, G.T.: Proximity Graphs for Nearest Neighbor Decision Rules: Recent Progress. In: 34 Symposium on Computing and Statistics INTERFACE 2002 (2002)
Martínez-Trinidad, J.F., Ruiz-Shulcloper, J., Lazo-Cortés, M.S.: Structuralization of universes. Fuzzy sets and systems 112, 485–500 (2000)
Tomek, I.: Two modifications of CNN. IEEE Transactions on systems, man and cybernetics SMC-6, 769–772 (1976)
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)
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)
Skalak, D.B.: Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms. In: Eleventh International Conference on Machine Learning (1994)
Kibler, D., Aha, D.W.: Learning representative exemplars of concepts: An initial case study. In: Fourth international workshop on Machine learning, pp. 24–30 (1987)
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
García-Borroto, M., Ruiz-Shulcloper, J. (2005). Selecting Prototypes in Mixed Incomplete Data. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_47
Download citation
DOI: https://doi.org/10.1007/11578079_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
eBook Packages: Computer ScienceComputer Science (R0)
Publish with us
-
Published in cooperation with
http://www.iapr.org/
