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Autonomous clustering for machine learning

  • Oscar Luaces
  • Juan José del Coz
  • José Ramón Quevedo
  • Jaime Alonso
  • José Ranilla
  • Antonio Bahamonde
Plasticity Phenomena (Maturing, Learning & Memory)
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1606)

Abstract

In this paper, starting from a collection of training examples, we show how to produce a very compact set of classification rules. The induction idea is a clustering principle based on Kohonen’s self-organizing algorithms. The function to optimize in the aggregation of examples to become rules is a classificatory quality measure called impurity level, which was previously employed in our system called Fan. The rule conditions obtained in this way are densely populated areas in the attribute space. The main goal of our system, in addition to its accuracy, is the high quality of explanations that it can provide attached to the classification decisions.

Keywords

Classification Rule Impurity Level Original Training Cross Validation Experiment Symbolic Attribute 
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.

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References

  1. 1.
    Aha, D. W.: A Study of Instance-based Algorithms for Supervised Learning Tasks: Mathematical, Empirical and Psychological Evaluations. Ph. D. Dissertation, University of California at Irvine (1990).Google Scholar
  2. 2.
    Bahamonde, A., De La Cal, E. A., Ranilla, J., & Alonso, J.: Self-organizing symbolic learned rules. In: Mira, J., Moreno-Díaz, R., Cabestany (eds.): Biological and Artificial Computation: From Neuroscience to Technology. Lecture Notes in Computer Science, Vol. 1240. Springer-Verlag, Berlin Heidelberg New York (1997) 536–545CrossRefGoogle Scholar
  3. 3.
    Blake, C., Keogh, E., & Merz, C.J.: UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.htmlurl]. Irvine, CA: University of California, Department of Information and Computer Science (1998)Google Scholar
  4. 4.
    Clark, P., & Niblett, T.: The CN2 Induction Algorithm. Machine Learning, 3 (1988) 261–284Google Scholar
  5. 5.
    Cover, T. M., & Hart, P. E.: Nearest Neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), (1967) 21–27CrossRefzbMATHGoogle Scholar
  6. 6.
    Del Coz, J. J., Luaces, O., Quevedo, J.R., Alonso, J., & Bahamonde, A.: Self-Organizing Cases to Find Paradigms. Technical Report, Artificial Intelligence Center, University of Oviedo at Gijón. January (1999). [ftp://ftp.aic.uniovi.es/publications/Machine_Learning/paradigms.ZIP]Google Scholar
  7. 7.
    Fisher, R.: The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7, 1, (1936) 179–188CrossRefGoogle Scholar
  8. 8.
    Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11 (1993) 63–91CrossRefzbMATHGoogle Scholar
  9. 9.
    Kohavi, R., John, G., Long, R., Manley, D., & Pfleger, K.: MLC++: A machine learning library in C++. In: Proceedings of the Sixth International Conference on Tools with Artificial Intelligence. IEEE Computer Society Press (1994) 740–743Google Scholar
  10. 10.
    Kohonen, T.: Self-Organizing Maps. Springer-Verlag, Berlin Heidelberg New York (1995)CrossRefzbMATHGoogle Scholar
  11. 11.
    Luaces, O., Alonso, J., De La Cal, E. A., Ranilla, J., & Bahamonde, A.: Machine Learning usefulness relies on accuracy and self-maintenance. In: Pobil, A.P., Mira, J., Ali, M. (eds.): Tasks and Methods in Applied Artificial Intelligence. Lecture Notes in Artificial Intelligence, Vol. 1416. Springer-Verlag, Berlin Heidelberg New York (1998) 448–457CrossRefGoogle Scholar
  12. 12.
    Murthy, S. K., Kasif, S., & Salzberg, S.: A system for induction of oblique decision trees. Journal of Artificial Intelligence Research, 2 (1994) 1–32.zbMATHGoogle Scholar
  13. 13.
    Quinlan, J. R.: C4.5: Programs for Machine Learningg. Morgan Kaufmann, San Mateo, CA (1993)Google Scholar
  14. 14.
    Ranilla, J., & Bahamonde, A.: Fan: Finding Accurate iNductions. Technical Report, Artificial Intelligence Center, University of Oviedo at Gijón. November (1998) [ftp://ffp.aic.uniovi.es/publications/Machine_Learning/Fanprn.ZIP]Google Scholar
  15. 15.
    Ranilla, J., Mones, R., & Bahamonde, A.: El Nivel de Impureza de una regla de clasificación aprendida a partir de ejemplos. Revista Iberoamericana de Inteligencia Artificial, 4 (1998) 4–11.Google Scholar
  16. 16.
    Salzberg, S.: Learning with nested generalized exemplars. Kluwer Academic Publishers, Boston, MA (1990)CrossRefzbMATHGoogle Scholar
  17. 17.
    Spiegel, M. R. (1970): Estadística McGraw-Hill, Atlacomulco, México (1970)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Oscar Luaces
    • 1
  • Juan José del Coz
    • 1
  • José Ramón Quevedo
    • 1
  • Jaime Alonso
    • 1
  • José Ranilla
    • 1
  • Antonio Bahamonde
    • 1
  1. 1.Centro de Inteligencia Artificial. Universidad de Oviedo at GijónGijónEspaña

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