Advertisement

Learning to Assess from Pair-Wise Comparisons

  • J. Díez
  • J. J. del Coz
  • O. Luaces
  • F. Goyache
  • J. Alonso
  • A. M. Peña
  • A. Bahamonde
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2527)

Abstract

In this paper we present an algorithm for learning a function able to assess objects. We assume that our teachers can provide a collection of pairwise comparisons but encounter certain difficulties in assigning a number to the qualities of the objects considered. This is a typical situation when dealing with food products, where it is very interesting to have repeatable, reliable mechanisms that are as objective as possible to evaluate quality in order to provide markets with products of a uniform quality. The same problem arises when we are trying to learn user preferences in an information retrieval system or in configuring a complex device. The algorithm is implemented using a growing variant of Kohonen’s Self-Organizing Maps (growing neural gas), and is tested with a variety of data sets to demonstrate the capabilities of our approach.

Keywords

Neural Information Processing System Assessment Function Information Retrieval System Artificial Intelligence Research Regression Rule 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blake, C., Merz, C. J.: UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science (1998)
  2. 2.
    Bollen A.F., Kusabs N.J., Holmes G. and Hall M.A.: Comparison of consumer and producer perceptions of mushroom quality. Proc. Integrated View of Fruit and Vegetable Quality International Multidisciplinary Conference, W.J. Florkowski, S.E. Prussia and R.L. Shewfelt (eds.), Georgia (2002) 303–311Google Scholar
  3. 3.
    Branting, K.L., Broos, P.S.: Automated Acquisition of User Preferences. International Journal of Human-Computer Studies, (1997) 46:55–77.CrossRefGoogle Scholar
  4. 4.
    Branting, K.L.: Active Exploration in Instance-Based Preference Modeling, Proceedings of the Third International Conference on Case-Based Reasoning (ICCBR-99), Germany (1999)Google Scholar
  5. 5.
    Cohen, W.W., Shapire, R.E., Singer, Y.: Learning to order things. Journal of Artificial Intelligence Research (1999) 10, 243–270zbMATHMathSciNetGoogle Scholar
  6. 7.
    Del Coz, J. J., Luaces, O., Quevedo, J. R., Alonso, J., Ranilla, J., Bahamonde, A.: Self-Organizing Cases to Find Paradigms. Lecture Notes in Computer Sciences, Springer-Verlag, Berlin (1999) Vol. 1606, 527–536Google Scholar
  7. 8.
    Fritzke, B.: A growing neural gas network learns topologies, Advances in Neural Information Processing Systems 7, G. Tesauro, D. S. Touretzky and T. K. Leen (eds.), MIT Press, Cambridge MA (1995) 625–632Google Scholar
  8. 9.
    Goyache, F., Bahamonde, A. Alonso, J., López, S., Alonso, J., del Coz J.J., Quevedo, J.R., Ranilla, J., Luaces, O., Alvarez, I., Royo, L. and Díez J.: The usefulness of Artificial Intelligence techniques to assess subjective quality of products in the food industry. Trends in Food Science and Technology, in press (2002)Google Scholar
  9. 10.
    Goyache, F., del Coz, J.J., Quevedo, J.R., López, S., Alonso, J., Ranilla, J., Luaces, O., Alvarez, I. and Bahamonde, A.: Using artificial intelligence to design and implement a morphological assessment system in beef cattle. Animal Science (2001), 73: 49–60Google Scholar
  10. 11.
    Kohonen, T.: Self-Organizing Maps. Springer Series of Information Science. Springer-Verlag, Berlin (1995)Google Scholar
  11. 12.
    Kusabs N., Bollen F., Trigg L., Holmes G., Inglis S.: Objective measurement of mushroom quality. Proc. New Zealand Institute of Agricultural Science and the New Zealand Society for Horticultural Science Annual Convention, Hawke’s Bay, New Zealand (1998)Google Scholar
  12. 13.
    Meilgaard, M., Civille, G.V., Carr, B.T.: Sensory evaluation techniques. CRC Press, Inc., Boca Raton, Florida (1987)Google Scholar
  13. 14.
    Murthy, S. K., Kasif, S., Salzberg, S.: A system for induction of oblique decision trees. Journal of Artificial Intelligence Research, (1994) 2, 1–32zbMATHCrossRefGoogle Scholar
  14. 15.
    Quevedo, J.R., Bahamonde, A.: Aprendizaje de Funciones Usando Inducción sobre Clasificaciones Discretas. Proceedings CAEPIA’99 VIII Conferencia de la Asociación Española para la Inteligencia Artificial, Murcia, Spain (1999) Vol. I, 64–71Google Scholar
  15. 16.
    Quinlan, J. R.: Learning with continuous classes. Proceedings 5th Australian Joint Conference on Artificial Intelligence. World Scientific, Singapore, (1992), 343–348.Google Scholar
  16. 17.
    Tesauro, G.: Connectionist learning of expert preferences by comparison training. In Advances in Neural Information Processing Systems, Proceedings NIPS’88, MIT Press (1989) 99–106Google Scholar
  17. 18.
    Utgoff, J. P., Clouse, J.: Two kinds of training information for evaluation function learning. In Proceedings AAAI’91, MIT Press (1991) 596–600Google Scholar
  18. 19.
    Utgoff, J.P., Saxema, S.: Learning preference predicate. In Proceedings of the Fourth International Workshop on Machine Learning, Morgan Kaufmann, San Francisco (1987) 115–121Google Scholar
  19. 20.
    Wang Y., Witten I.H.: Inducing of Model Trees for Predicting Continuous Classes. Proceedings of European Conference on Machine Learning. Prague, Czech Republic (1997) 128–137.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • J. Díez
    • 1
  • J. J. del Coz
    • 2
  • O. Luaces
    • 2
  • F. Goyache
    • 1
  • J. Alonso
    • 2
  • A. M. Peña
    • 3
  • A. Bahamonde
    • 2
  1. 1.SERIDA-CENSYRA-SomióGijón (Asturias)Spain
  2. 2.Centro de Inteligencia ArtificialUniversidad de Oviedo at Gijón, Campus de ViesquesGijón (Asturias)Spain
  3. 3.Facultad de IngenieríaUniversidad Distrital Francisco José de CaldasBogotáColombia

Personalised recommendations