A Comparative Study of FCA-Based Supervised Classification Algorithms

  • Huaiyu Fu
  • Huaiguo Fu
  • Patrik Njiwoua
  • Engelbert Mephu Nguifo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2961)

Abstract

Several FCA-based classification algorithms have been proposed, such as GRAND, LEGAL, GALOIS, RULEARNER, CIBLe, and CLNN & CLNB. These classifiers have been compared to standard classification algorithms such as C4.5, Naïve Bayes or IB1. They have never been compared each other in the same platform, except between LEGAL and CIBLe. Here we compare them together both theoretically and experimentally, and also with the standard machine learning algorithm C4.5. Experimental results are discussed.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dietterich, T.: Approximate statistical tests for comparing supervised classification learning algorithms. Research report, Computer Science Dept., Oregon State University (1997)Google Scholar
  2. 2.
    Eklund, P.W.: A performance survey of public domain supervised machine learning algorithms. Technical report, The University of Wollongong, Australia (2002)Google Scholar
  3. 3.
    Xie, Z., Hsu, W., Liu, Z., Lee, M.: Concept lattice based composite classifiers for high predictability. Journal of Experimental and Theoretical Artificial Intelligence 14, 143–156 (2002)MATHCrossRefGoogle Scholar
  4. 4.
    Birkhoff, G.: Lattice Theory, 3rd edn. American Mathematical Society, Providence (1967)MATHGoogle Scholar
  5. 5.
    Ganter, B., Wille, R.: Formal Concept Analysis. Mathematical Foundations. Springer, Heidelberg (1999)MATHGoogle Scholar
  6. 6.
    Oosthuizen, G.: The application of concept lattices to machine learning. Technical Report CSTR 94/01, Department of Computer Science, University of Pretoria, Pretoria, South Africa (1994)Google Scholar
  7. 7.
    Liquière, M., Mephu Nguifo, E.: LEGAL: LEarning with GAlois lattice. In: Actes des Journées Françaises sur l’Apprentissage (JFA), Lannion, France, pp. 93–113 (1990)Google Scholar
  8. 8.
    Carpineto, C., Romano, G.: Galois: An order-theoretic approach to conceptual clustering. In: Proceedings of ICML 1993, Amherst, pp. 33–40 (1993)Google Scholar
  9. 9.
    Sahami, M.: Learning Classification Rules Using Lattices. In: Lavrač, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912, pp. 343–346. Springer, Heidelberg (1995)Google Scholar
  10. 10.
    Njiwoua, P., Mephu Nguifo, E.: Améliorer l’apprentissage à partir d’instances grâce à l’induction de concepts: le système cible. Revue d’Intelligence Artificielle (RIA) 13, 413–440 (1999)Google Scholar
  11. 11.
    Bordat, J.: Calcul pratique du treillis de galois d’une correspondance. Mathématiques, Informatiques et Sciences Humaines 24, 31–47 (1986)MathSciNetGoogle Scholar
  12. 12.
    Blake, C., Keogh, E., Merz, C.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Huaiyu Fu
    • 1
  • Huaiguo Fu
    • 1
  • Patrik Njiwoua
    • 1
  • Engelbert Mephu Nguifo
    • 1
  1. 1.CRIL-CNRS FRE2499Université d’Artois – IUT de LensLens cedexFrance

Personalised recommendations