Advertisement

Selecting Prototypes in Mixed Incomplete Data

  • Milton García-Borroto
  • José Ruiz-Shulcloper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

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.

References

  1. 1.
    Martínez-Trinidad, F., Guzmán-Arenas, A.: The logical combinatorial approach to Pattern Recognition, an overview through selected works. Pattern Recognition 34, 741–751 (2001)zbMATHCrossRefGoogle Scholar
  2. 2.
    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, USAGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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, SingaporeGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Hart, P.E.: The condensed nearest neighbor rule. IEEE Trans. on Information Theory 14, 515–516 (1968)CrossRefGoogle Scholar
  7. 7.
    Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on systems, man and cybernetics SMC-2, 408–421 (1972)zbMATHCrossRefGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Kim, S.-W., Oommen, J.B.: A brief taxonomy and ranking of creative prototype reduction schemes. In: IEEE SCM Conference (2002)Google Scholar
  10. 10.
    Toussaint, G.T.: Proximity Graphs for Nearest Neighbor Decision Rules: Recent Progress. In: 34 Symposium on Computing and Statistics INTERFACE 2002 (2002)Google Scholar
  11. 11.
    Martínez-Trinidad, J.F., Ruiz-Shulcloper, J., Lazo-Cortés, M.S.: Structuralization of universes. Fuzzy sets and systems 112, 485–500 (2000)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Tomek, I.: Two modifications of CNN. IEEE Transactions on systems, man and cybernetics SMC-6, 769–772 (1976)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)Google Scholar
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    Skalak, D.B.: Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms. In: Eleventh International Conference on Machine Learning (1994)Google Scholar
  16. 16.
    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)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Milton García-Borroto
    • 1
  • José Ruiz-Shulcloper
    • 2
  1. 1.Bioplants Center, UNICA, C. de ÁvilaCuba
  2. 2.Advanced Technologies Applications Center, MINBASCuba

Personalised recommendations