Advertisement

Supervised Classification Using Homogeneous Logical Proportions for Binary and Nominal Features

  • Ronei M. Moraes
  • Liliane S. Machado
  • Henri Prade
  • Gilles Richard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

The notion of homogeneous logical proportions has been recently introduced in close relation with the idea of analogical proportion. The four homogeneous proportions have intuitive meanings, which can be related with classification tasks. In this paper, we proposed a supervised classification algorithm using homogeneous logical proportions and provide results for all. A final comparison with previous works using similar methodologies and with other classifiers is provided.

Keywords

supervised classification analogical proportion analogical dissimilarity 

References

  1. 1.
    Bache, K., Lichman, M.: UCI machine learning repository, http://archive.ics.uci.edu/ml (2013)
  2. 2.
    Bayoudh, S., Miclet, L., Delhay, A.: Learning by analogy: A classification rule for binary and nominal data. In: Proc. Inter. Conf. on Artificial Intelligence, IJCAI 2007, pp. 678–683 (2007)Google Scholar
  3. 3.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience (2001)Google Scholar
  4. 4.
    Hall, M., et al.: The Weka data mining software: An update. SIGKDD Explorations 11, 10–18 (2009)CrossRefGoogle Scholar
  5. 5.
    Hüllermeier, E.: Case-Based Approximate Reasoning. Theory and Decision Library. Springer, New York (2007)Google Scholar
  6. 6.
    Miclet, L., Delhay, A.: Analogical Dissimilarity: definition, algorithms and first experiments in machine learning. Technical Report 5694, IRISA (September 2005)Google Scholar
  7. 7.
    Miclet, L., Bayoudh, S., Delhay, A.: Analogical dissimilarity: definition, algorithms and two experiments in machine learning. JAIR 32, 793–824 (2008)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Miclet, L., Prade, H.: Handling analogical proportions in classical logic and fuzzy logics settings. In: Sossai, C., Chemello, G. (eds.) ECSQARU 2009. LNCS, vol. 5590, pp. 638–650. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Moraes, R.M., Machado, L.S., Prade, H., Richard, G.: Classification based on homogeneous logical proportions. Proc. 33th Int. Conf. on Artificial Intelligence (AI 2013), Cambridge (to appear, 2013)Google Scholar
  10. 10.
    Prade, H., Richard, G.: Analogy, paralogy and reverse analogy: Postulates and inferences. In: Mertsching, B., Hund, M., Aziz, Z. (eds.) KI 2009. LNCS, vol. 5803, pp. 306–314. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Prade, H., Richard, G.: Analogical proportions: another logical view. In: Proc. 29th Int. Conf. Artif. Intellig (AI 2009), Cambridge, pp. 121–134. Springer (2009)Google Scholar
  12. 12.
    Prade, H., Richard, G.: Multiple-valued logic interpretations of analogical, reverse analogical, and paralogical proportions. In: Proc. 40th IEEE International Symp. on Multiple-Valued Logic (ISMVL 2010), Barcelona, pp. 258–263 (2010)Google Scholar
  13. 13.
    Prade, H., Richard, G., Yao, B.: Enforcing regularity by means of analogy-related proportions - A new approach to classification. Int. Jour. Computer Information Systems and Industrial Management Applications 4, 648–658 (2012)Google Scholar
  14. 14.
    Prade, H., Richard, G.: Homogeneous Logical Proportions: Their Uniqueness and Their Role in Similarity-Based Prediction. In: Proc. XIII International Conference on Principles of Knowledge Representation and Reasoning, pp. 402–412 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ronei M. Moraes
    • 1
    • 2
  • Liliane S. Machado
    • 1
    • 2
  • Henri Prade
    • 2
  • Gilles Richard
    • 2
  1. 1.LabTEVEFederal University of ParaibaJoao PessoaBrazil
  2. 2.IRITUniversity of ToulouseToulouse Cedex 09France

Personalised recommendations